diagrams photographs, illustrations and maps may be used to enhance the text; reports don't usually have an "ending", although sometimes the detailed information is rounded off by some general statement about the topic. Language. Nouns and noun phrases are used rather than personal pronouns. The use of personal pronouns is limited. 39/5. Rate. Start at $19.9 Min delivery time 6 hrs. Read the review Visit the website. TranslateHub is one of the best translation services we've tested, and here is why. While it has been working only since 2019, it managed to gather a team of true experts with years of verified experience and top qualifications. Atext is any stretch of language that can be understood in context. It may be as simple as 1-2 words (such as a stop sign) or as complex as a novel. Any sequence of sentences that belong together can be considered a text. Text refers to content rather than form; for example, if you were talking about the text of "Don Quixote," you would be 1Concept of Marugoto. Marugoto Japanese Language and Culture (Marugoto) is a set of learning materials created with the idea of putting into practice Japanese language education that promotes mutual understanding.This is done by cultivating in learners an ability to achieve goals through language communication and an attitude where they understand and respect both their own and other cultures. Themain difference between introduction and literature review is their purpose; the purpose of an introduction is to briefly introduce the text to the readers whereas the purpose of a literature review is to review and critically evaluate the existing research on a selected research area. In this article, we will be discussing, 1. Thishelps the reader better learn and understand the subject. 6. The titles all talk about space so the articles are all about space. Titles • Titles tell the reader the topic of the text. • Titles show the main idea of the text. • Titles help the reader by letting them know what they are about to read. . English Writing Text Types Imaginative Writing Non-Editable Non-Editable PDF Pages Pages 1 Curriculum Curriculum AUS V9, AUS V8, NSW, VIC Year Year 3 - 6 A poster about reviews, including an annotated example. Use this teaching resource to remind your students about the structure and language features to use when writing a review. The black and white version can be printed at a smaller size for students to include in their notebooks. Curriculum AC9E3LY03 Identify the audience and purpose of imaginative, informative and persuasive texts through their use of language features and/or images AC9E4LY03 Identify the characteristic features used in imaginative, informative and persuasive texts to meet the purpose of the text AC9E5LY03 Explain characteristic features used in imaginative, informative and persuasive texts to meet the purpose of the text AC9E6LY03 Analyse how text structures and language features work together to meet the purpose of a text, and engage and influence audiences Teach Starter Publishing We create premium quality, downloadable teaching resources for primary/elementary school teachers that make classrooms buzz! Find more resources like this EnglishWritingText TypesImaginative WritingReview TextsPosters Year 3Year 4Year 5Year 6 PDF teaching resource Story Characters - Mini Book Teach your little learners about the various types of story characters with this fun-sized mini-book. teaching resource Exploring Story Characters - Worksheets Explore the defining features of story characters with this differentiated worksheet. teaching resource Character or Not? - Sorting Activity Explore the difference between characters and non-characters with this hands-on sorting activity. teaching resource Character or Not? - Interactive Activity Explore the difference between characters and non-characters with this digital learning activity. teaching resource Listening to Others – Discussion Task Cards and Poster Give students the opportunity to work on their listening skills and learn what it means to be a good listener with this set of 42 discussion cards and classroom poster. teaching resource Story Setting or Not? Cut and Paste Worksheet Explore the difference between story settings and non-settings with this cut-and-paste worksheet. teaching resource Character or Not? Cut and Paste Worksheet Explore the difference between characters and non-characters with this cut-and-paste worksheet. teaching resource Character or Not? - Colouring Worksheet Explore the difference between characters and non-characters with this colouring worksheet. teaching resource Affixes Puzzle Activity Build words with affixes with a pack of printable word-building puzzles. teaching resource Narrative Elements - Worksheet Practise identifying characters, settings, problems and solutions in fictional texts with this set of worksheets. Your current page is in Australia Review Text Type Poster With Annotations in United States Review Text Type Poster With Annotations in United Kingdom Language Features of a Critical Review Writing a Critical Review Here is a sample extract from a critical review of an article. Only the introduction and conclusion are included. Parts of the Review have been numbered [1] – [12]. Read the extract and match them with the language features listed here a Concessive clauses assist in expressing a mixed response b Conclusion summarises reviewer’s judgement c Introduction d Modality used to express certainty and limit overgeneralising e Offers recommendations f Presents the aim/purpose of the article and Key findings g Qualifies reviewer’s judgement h Reporting verbs i Reviewer ’s judgement j Sentence themes focus on the text k Title and bibliographic details of the text l Transition signals provide structure and coherence [1] A Critical Review of Goodwin et al, 2000, 'Decision making in Singapore and Australia the influence of culture on accountants’ ethical decisions', Accounting Research Journal, no. 2, pp 22-36. [2] Using Hofstede’s 1980, 1983 and 1991 and Hofstede and Bond’s 1988 five cultural dimensions, Goodwin et al 2000 conducted [3] a study on the influence of culture on ethical decision making between two groups of accountants from Australia and Singapore. [4] This research aimed to provide further evidence on the effect of cultural differences since results from previous research have been equivocal. [5] The study reveals that accountants from the two countries responded differently to ethical dilemmas in particular when the responses were measured using two of the five cultural dimensions. The result agreed with the prediction since considerable differences existed between these two dimensions in Australians and Singaporeans Hofstede 1980, 1991. [6] However the results of the other dimensions provided less clear relationships as the two cultural groups differed only slightly on the dimensions. [7] To the extent that this research is exploratory, results of this study provide insights into the importance of recognising cultural differences for firms and companies that operate in international settings. However several limitations must be considered in interpreting the study findings. …. [8] In summary, it has to be admitted that the current study is [9] still far from being conclusive. [10] Further studies must be undertaken, better measures must be developed, and larger samples must be used to improve our understanding concerning the exact relationship between culture and decision making.[11] Despite some deficiencies in methodology,[12] to the extent that this research is exploratory trying to investigate an emerging issue, the study has provided some insights to account for culture in developing ethical standards across national borders. Due to the development of e-commerce and web technology, most of online Merchant sites are able to write comments about purchasing products for customer. Customer reviews expressed opinion about products or services which are collectively referred to as customer feedback data. Opinion extraction about products from customer reviews is becoming an interesting area of research and it is motivated to develop an automatic opinion mining application for users. Therefore, efficient method and techniques are needed to extract opinions from reviews. In this paper, we proposed a novel idea to find opinion words or phrases for each feature from customer reviews in an efficient way. Our focus in this paper is to get the patterns of opinion words/phrases about the feature of product from the review text through adjective, adverb, verb, and noun. The extracted features and opinions are useful for generating a meaningful summary that can provide significant informative resource to help the user as well as merchants to track the most suitable choice of IntroductionMuch of the existing research on textual information processing has been focused on mining and retrieval of factual information. Little works had been done on the process of mining opinions until only recently. Automatic extraction of customers’ opinions can better benefit both customers and manufacturers. Product review mining can provide effective information that are classifying customer reviews as “recommended” or “not recommended” based on customers’ opinions for each product feature. In this cases, customer reviews highlight opinion about product features from various Merchant sites. However, many reviews are so long and only a few sentences contain opinions for product a popular product, the number of reviews can be in hundreds or even in thousands, which is difficult to be read one by one. Therefore, automatic extraction and summarization of opinion are required for each feature. Actually, when a user expresses opinion for a product, he/she states about the product as a whole or about its features one by one. Feature identification in product is the first step of opinion mining application and opinion words extraction is the second step which is critical to generate a useful summary by classifying polarity of opinion for each feature. Therefore, we have to extract opinion for each feature of a this paper, we take a written review as input and produce a summary review as output. Given a set of customer reviews of a particular product, we need to perform the following tasks1identifying product feature that customer commented on;2extracting opinion words or phrases through adjective, adverb, verb, and noun and determining the orientation;3generating the use a part-of-speech tagger to identify phrases in the input text that contains adjective or adverb or verb or nouns as opinion phrases. A phrase has a positive semantic orientation when it has good associations “awesome camera” and a negative semantic orientation when it has bad associations “low battery”.The rest of the paper is organized as follows. Section 2 describes the related work of this paper. Section 3 elaborates theoretical background for opinion mining. Section 4 expresses methodology and experiments of the system and Section 5 describes are several techniques to perform opinion mining tasks. In this section, we discuss others’ related works for feature extraction and opinion words extraction. Hu and Liu [1] proposed several methods to analyze customer reviews of format 3. They perform the same tasks of identifying product features on which customers have expressed their opinions and determining whether the opinions are positive or negative. However, their techniques, which are primarily based on unsupervised item sets mining or association rule mining, are only suitable for reviews of formats 3 and 1 to extract product features. Then, frequent item sets of nouns in reviews are likely to be product features while the infrequent ones are less likely to be product features. This work also introduced the idea of using opinion words to find additional often infrequent of these formats usually consist of full sentences. The techniques are not suitable for pros and cons of format 2, which are very brief. Liu et al. [2] presented how to extract product features from “Pros” and “Cons” as type of review format 2. They proposed a supervised pattern mining method to find language patterns to identify product features. They do not need to determine opinion orientations because of using review format 2 indicated by “Pros” and “Cons.”Hu and Liu [3] proposed a number of techniques based on data mining and natural language processing methods to mine opinion/product features. It is mainly related to text summarization and terminology identification. Their system does not mine product features and their work does not need a training corpus to build a summary. Su et al. [4] proposed a novel mutual reinforcement approach to deal with the feature-level opinion mining problem. Their approach predicted opinions relating to different product features without the explicit appearance of product feature words in reviews. They aim to mine the hidden sentiment link between product features and opinion words and then build the association approach for mining product feature and opinion based on consideration of syntactic information and semantic information in [5]. The methods acquire relations based on fixed position of words. However, the approaches are not effective for many cases. Turney [6] presented a simple unsupervised learning algorithm for classifying reviews as recommended thumbs up or not recommended thumbs down. The classification of a review is predicted by the average semantic orientation of the phrases in the review that contains adjectives or adverbs. Wu et al. [7] implemented extracting relations between product feature and expressions of opinions. The relation extraction is an important subtask of opinion mining for the relations between more than one product features and different opinion words on each of and Lam [8, 9] employ hidden Markov models and conditional random fields, respectively, as the underlying learning method for extracting product features. Pang et al. [10], Mras and Carroll [11], and Gamon [12] use the data of movie review, customer feedback review, and product review. They use the several statistical feature selection methods and directly apply the machine learning techniques. These experiments show that machine learning techniques only are not well performing on sentiment classification. They show that the presence or absence of word seems to be more indicative of the content rather than the frequency for a word. Zhang and Liu [13] aimed to identify such opinionated noun features. Their involved sentences are also objective sentences but imply positive or negative opinions. They proposed a method to deal with the problem for finding product features which are nouns or noun phrases that are not subjective but Mining Opinion for Feature LevelIn this paper, we only focus on mining opinions for feature level. This task is not only technically challenging because of the need for natural language processing, but also very useful in practice. For example, businesses always want to find public or consumer opinions about their products and services from the commercial web sites. Potential customers also want to know the opinions of existing users before they use a service or purchase a product. Moreover, opinion mining can also provide valuable information for placing advertisements in commercial web pages. If in a page people express positive opinions or sentiments on a product, it may be a good idea to place an ad of the product. However, if people express negative opinions about the product, it is probably not wise to place an ad of the product. A better idea may be to place an ad of a competitor’s are three main review formats on the Web. Different review formats may need different techniques to perform the opinion extraction 1—pros and cons The reviewer is asked to describe pros and cons 2—pros, cons, and detailed review the reviewer is asked to describe pros and cons separately and also write a detailed 3—free format the reviewer can write freely, that is, no separation of pros and the review formats 1 and 2, opinion or semantic orientations positive or negative of the features are known because pros and cons are separated. Only product features need to be identified. We concentrate on review format 3 and we need to identify and extract both product features and opinions. This task goes to the sentence level to discover details, that is, what aspects of an object that people liked or disliked. The object could be a product, a service, a topic, an individual, an organization, and so forth. For instance, in a product review sentence, it identifies product features that have been commented on by the reviewer and determines whether the comments are positive or negative. For example, in the sentence, “The battery life of this camera is too short,” the comment is on “battery life” of the camera object and the opinion is real-life applications require this level of detailed analysis because, in order to make product improvements, one needs to know what components and/or features of the product are liked and disliked by consumers. Such information is not discovered by sentiment and subjectivity classification [14]. To obtain such detailed aspects, we need to go to the sentence level. Two tasks are apparent.1Identifying and extracting features of the product that the reviewers have expressed their opinions on, called product features for instance, in the sentence “the picture quality of this camera is amazing,” the product feature is “picture quality.”2Determining whether the opinions on the features are positive, negative or neutral. In the above sentence, the opinion on the feature “picture quality” is the sentence, “the battery life of this camera is too short,” the comment is on the “battery life” and the opinion is negative. A structured summary will also be produced from the mining Methodology to Find Patterns for Features and Opinions ExtractionThe goal of OM is to extract customer feedback data such as opinions on products and present information in the most effective way that serves the chosen objectives. Customers express their opinion in review sentences with single words or phrases. We need to extract these opinion words or phrases in efficient way. Pattern extraction approach is useful for commercial web pages in which customers can be able to write comments about products or services. Let us use an example of the following review sentence “The battery life is long.”In this sentence, the feature is “battery life” and opinion word is “long.” Therefore, we first need to identify the feature and opinion from the 1 shows the overall process for generating the results of feature-based opinion summarization. The system input is customer reviews’ datasets. We first need to perform POS tagging to parse the sentence and then identify product features and opinion words. The extracted opinion words/phrases are used to determine the opinion orientation which is positive or negative. Finally, we summarize the opinion for each product feature based on their this paper, we focus on feature extraction and opinion word extraction to provide opinion summarization. In feature extraction phase, we need to perform part-of-speech tagging to identify nouns/noun phrases from the reviews that can be product features. Nouns and noun phrases are most likely to be product tagging is important as it allow us to generate general language patterns. We use Stanford-POS tagger to parse each sentence and yield the part-of-speech tag of each word whether the word is a noun, adjective, verb, adverb, etc. and identify simple noun and verb groups syntactic chunking, for instance,The_DT photo_JJ quality_NN is_VBZ amazing_JJ and_CC i_FW know_VBP i_FW m_VBP going_VBG to_TO have_VB fun_NN with_IN all_PDT the_DT POS tagging is done, we need to extract features that are nouns or noun phrases using the pattern knowledge see Table 1. And then, we focus on identifying domain product features that are talked about by customers by using the manually tagged training corpus for domain opinion words extraction, we used extracted features that are used to find the nearest opinion words with adjective/adverb. To decide the opinion orientation of each sentence, we need to perform three subtasks. First, a set of opinion words adjectives, as they are normally used to express opinions is identified. If an adjective appears near a product feature in a sentence, then it is regarded as an opinion word. We can extract opinion words from the review using the extracted features, for instance;The strap is horrible and gets in the way of parts the camera you need access nearly 800 pictures I have found that this camera takes incredible comes with a rechargeable battery that does not seem to last all that long, especially if you use the flash a the first sentence, the feature, strap, is near the opinion word horrible. And in the second example, feature “picture” is close to the opinion word incredible. We found that opinion words/phrases are mainly adjective/adverb that is used to qualify product features with nouns/noun phrases. In this case, we can extract the nearby adjective as opinion word if the sentences contain any features. However, for the third sentence, the feature, battery, cannot be able to extract nearby adjective to meet the opinion word “long.” The nearby adjective “rechargeable” dose not bear opinion for the feature “battery.”Moreover, both adjective and adverb are good indicators of subjectivity and opinions. Therefore, we need to extract phrases containing adjective, adverb, verb, and noun that imply opinion. We also consider some verbs like, recommend, prefer, appreciate, dislike, and love as opinion words. Some adverbs like not, always, really, never, overall, absolutely, highly, and well are also considered. Therefore, we extract two or three consecutive words from the POS-tagged review if their tag conforms to any of the patterns. We collect all opinionated phrases of mostly 2/3 words like adjective, noun, adjective, noun, noun, adverb, adjective, adverb, adjective, noun, verb, noun, and so forth from the processed POS-tagged resulting patterns are used to match and identify opinion phrases for new reviews after the POS tagging. However, there are more likely opinion words/phrases in the sentence but they are not extracted by any patterns. From these extracted patterns, most of adjectives or adverbs imply opinion for the nearest nouns/noun phrases. Table 2 described some examples of opinion Dataset of the SystemWe used annotated customer reviews’ data set of 5 products for testing. All the reviews are from commercial web sites such as and Each review consists of review title and detail of review text. The reviews are retagged manually based on our own feature list. Each camera review sentence is attached with the mentioned features and their associated opinion words. Therefore, we only focus on the review sentences that contain opinions for product features, for instance, “The pictures are absolutely amazing—the camera captures the minutest of details.” This sentence will receive the tag picture [+3]. Words in the brackets are those we found to be associated with the corresponding opinion orientation of feature whether positive or negative see Table 3. ExperimentsWe carried out the experiments using customer reviews of 5 electronic products two digital cameras, one DVD player, one MP3 player, and one cellular phone. All the reviews are extracted from All of them are used as the training data to mine patterns. These patterns are then used to extract product features from test reviews of these products. We now evaluate the proposed automatic technique to see how effective it is in identifying product features and opinions from customer reviews. In this paper, we only verify only product features but we make sentiment orientation of opinion on that features as an ongoing process. The effectiveness of the proposed system has been verified with review set on these five different electronic products. All the results generated by our system are compared with the manually tagged results. We also assess the time saved by semiautomatic tagging over manual tagging. We showed the comparison results with Hu and Liu’s approach and our approach is slightly higher than their results in Table ConclusionMost of opinion mining researches use a number of techniques for mining opinion and summarizing opinions based on features in product reviews based on data mining and natural language processing methods. Review text is unstructured and only a portion or some sentences include opinion-oriented words. In product reviews, users write comments about features of products to describe their views according to their experience and observations. The first step of opinion mining in classifying reviews’ documents is extracting features and opinion words. Therefore opinion mining system needs only the required sentences to be processed to get knowledge efficiently and effectively. We proposed the ideas to extract patterns of features and/or opinion phrases. We showed results of experiments with extracting pattern knowledge based on linguistic rule. We expected to achieve good results by extracting features and opinion-oriented words from review text with help of adjectives, adverbs, nouns, and verbs. We believe that there is rich potential for future research. For identifying feature, we need to extend both explicit and implicit feature as our future work because both of these features are useful for providing more accurate results in determining the polarity of product/feature before summarizing them, rather than explicit feature Hu and B. 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Liu, “Identifying noun product features that imply opinions,” in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics Human Language Technologies ACL-HLT '11, pp. 575–580, June at Google ScholarB. Liu, “Sentiment Analysis and Subjectivity,” in A Chapter in Handbook of Natural Language Processing, 2nd at Google ScholarCopyright © 2013 Su Su Htay and Khin Thidar Lynn. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Definition Review text is an evaluation of publication, such as a movie, video game, musical composition, book; a piece of hardware like a car, home appliance, or computer; or an event or performance, such as a live concert, a ply, musical theater show or dance show. Purpose Ø Review text is used to critic the events or art works for the reader or listener, such as movies, shows, book, and others. Ø To critique or evaluate an art work or event for a public audience Generic Structure ü Orientation/Introduction General information of the text. ü Interpretative Recount Summary of an art works including character and plot. ü Evaluation Concluding statement Judgment, opinion, or recommendation. It can consist of more than one. ü Summary The last opinion consist the appraisal or the punch line of the art works being criticized. Language Features Using the present tense Focus on specific participants Using adjectives form example like ad, good, valuable, etc. Using long and complex clauses Using metaphor Hallo everybody Have you ever reviewed things, movies, songs or something else? If you have not, have you seen a movie review or book review? You can see examples of review text on newspapers that show movies or book reviews, as an illustration of what the Review Text is. Review Text is supposedly the last English lesson of high school level. If you could not make an example of review text, it can be said that you have not passed National Exams, especially for English lessons. You don’t want to be said you could not pass the exam, right? Therefore, in order not to “be considered” to be failed in the journey during school, let us learn again what and how review text is. Ready? Definition of Review Text Review text is an evaluation of a publication, such as a movie, video game, musical composition, book; a piece of hardware like a car, home appliance, or computer; or an event or performance, such as a live music concert, a play, musical theatre show or dance show. Generic Structure of Review Text Orientation Background information of the text. Evaluations Concluding statement judgement, opinion, or recommendation. It can consist ot more than one. Interpretative Recount Summary of an art works including character and plot. Evaluative Summation The last opinion consisting the appraisal or the punch line of the art works being criticized. In other word Orientation places the work in its general and particular context, often by comparing it with others of its kind or through an analog with a non–art object or event. Interpretive Recount summarize the plot and/or providers an account of how the reviewed rendition of the work came into being Evaluation provides an evaluation of the work and/or its performance or production; is usually recursive Actually, the generic structure of text review does not have to be exactly same as above, perhaps for the reason of “summarizing” the lesson, so the three or four generic structure above just become general description about the structure in review text, okay. Still confused? I also still confused.. 🙂 Okay, let’s just discuss some examples of review text, which is hopefully can understand more about this kind of text. But before we go to the example of review text, let’s discuss its purpose and language features. Purpose of Review Text Review text is used to evaluate / review / critic the events or art works for the reader or listener, such as movies, shows, book, and others. Language Features of Review Text – Present tense. – Using long and complex clauses I just mention those language feature of review text above because those are the main language feature of review text that can be used to identify review text easily. Example of Review Text Example of Review Text Film about The Amazing Spiderman 2 Review of The Amazing Spiderman 2 Introduction I will start by saying that I am a huge fan of Spider-man. I love all the trilogies worked by Raimi yes, even the Spider-man 3 but I do not like the The Amazing Spiderman 1. I was skeptical when I wanted to watch this movie, but I was wrong and I think this second sequel is really great. Unlike its predecessor, this film is full of action, humor, and emotional. Played by the big players, the story is well-written. The action is really spectacular and the final scene makes me satisfied. Evaluation 1 / Interpretation The story begins when Peter Parker Andrew Garfield struggled to maintain his relationship with Gwen Emma Stone after her father’s death. His actions also cause the emergence of a new enemy, Electro, a villain played by James Foxx. Peter also continue to investigate what happened to his father and reunited with his old friend, Harry Osborn. This movie is ended by the death of Gwen that makes the audience will be very emotional and sad. Evaluation 2 However I have to criticize about this film addressed to Paul Giamatti who plays Rhino. His appearance is too over. His acting also does not show that he is a feared villain. It would be a serious problem for the next Spiderman series. So I hope he can improve his acting better than before. Summary Overall, I think this is the best superhero movie since the appearance of The Dark Knight Rises. The script is well-written and convincing. I am sure the next series will be outstanding superhero movie. I recommend this movie to anyone who loves Spider-man or other superhero movies. Example of Review Text Assalamu’alaikum Beijing Review Text of Assalamu’alaikum Beijing Novel Movie title Assalamu’alaikum Beijing Genre Romantic-Religious Director Guntur Soeharjanto Playwritter Asma Nadia Cast Revalina S. Temat, Morgan Oey, Ibnu Jamil, Laudya C. Bella, Desta, Ollyne Apple, Cynthia Ramlan, Jajang C. Noer I really love all the novels written by Asma Nadia. So when the Assalamu’alaikum Beijing novel is filmed , I can hardly wait for the movie in theater. Because it is certainly very good quality film is directed by Guntur Soeharjanto. The film with the tagline “If you do not find love, let love find you”. In accordance with the novel title, the film is a lot to discuss religion and love. So it is labeled as romantic religious genre. The film tells the love story that is experienced by Asmara Revalina S. Temat who was broken heart knowing her fiance, Dewa Ibn Jamil had an affair with her friend Anita Cynthia Ramlan just a day before the wedding took place. At the same time, finally Asthma received a job in Beijing due to the help of Sekar Laudya Cynthia Bella. On the way Asma met Zhongwen Morgan Oey. Asma began to open her heart to Zhongwen. However, before continuing their relationship, Asma was diagnosed APS, a syndrome that made her life in danger and could die at any time. Example of Review Text about Film – Film Merry Riana Mimpi Sejuta Dollar Director Hestu Saputra Producer Dhamoo Punjabi, Manoj Punjabi Cast Chelsea Islan, Dion Wiyoko, Kimberly Ryder, Ferry Salim, Niniek L Karim, Sellen Fernandez, Mike Muliyandro, Chyntia Lamusu Studio MD Pictures Released Date December 24, 2014 Duration 105 Minute Country Singapore, Indonesia Orientation Merry Riana is a successful young woman entrepreneur, writer, and motivator. Her life’s story is told in a movie, Merry Riana “Million Dollar Dream”, which is adapted from her book with the same title. This film visualizes her struggle to survive from difficulty of life and become successful woman. Evaluation The violence that happened in Jakarta and other big cities in Indonesia in May 1998 makes Merry Riana forced to flee to Singapore. Merry Riana’s father decided to send his daughter to Singapore because he was afraid of the unsafe condition. She went alone to Singapore with the support money that was only enough to buy food for five days. Fortunately, Merry Riana met with her best friend, Irene, who wanted to go to university there, too. With Irene’s help, Merry could live in a boarding house. She was also accepted in one of the best college there. But, it could only be reached if Merry paid $40,000. The only hope was to take a loan college student that could only be obtained if Merry had a guarantor. Then, Merry met her senior, Alva, who was very reckoning. He gave many requirements before he finally agreed to help Merry. He also had Merry look for side job. Merry realized that she should be successful as soon as possible. She did various work, from spreading online business brochure, until playing with high risky shares. The condition of her economy was moving up and down. Problem of love also occurred when Alva expressed his feeling to Merry. Meanwhile, Merry knew it well that Irene fell in love with Alva. Interpretation The acting of Chelsea Islan Merry Riana in that movie is very good. She can impersonate Merry Riana’s character very well. But, it would be better if there was no kissing scene. Summary I think this is an inspirational movie which can motivate people to be successful at young age. It brings good spirit for young men in Indonesia. The script writer is successful to bring a set of interesting conflicts which make the plot of this movie become alive. Arti dalam Bahasa Indonesia Film Merry Riana Mimpi Sejuta Dollar Sutradara Hestu Saputra Produser Dhamoo Punjabi, Manoj Punjabi Pemeran Chelsea Islan, Dion Wiyoko, Kimberly Ryder, Ferry Salim, Niniek L Karim, Sellen Fernandez, Mike Muliyandro, Chyntia Lamusu Studio MD Pictures Tanggal rilis December 24, 2014 Durasi 105 Menit Negara Singapore, Indonesia Baca juga 2 Procedure Text How To Make Fried Chicken dan Artinya Pengantar Merry Riana adalah pengusaha wanita muda, penulis, dan motivator yang sukses. Kisah hidupnya diceritakan dalam film “Merry Riana Mimpi Sejuta Dolar, yang diadaptasi dari bukunya dengan judul yang sama. Film ini memvisualisasikan bagaimana ia berjuang untuk bertahan dari kesulitan hidup dan menjadi sukses. Evaluasi Kerusuhan yang terjadi di Jakarta dan kota besar lainnya di Indonesia pada Mei 1998 membuat Merry Riana terpaksa mengungsi ke Singapura. Ayah Merry Riana memutuskan untuk mengirimkan anaknya ke Singapura karena takut dengan kondisi yang sedang tidak aman. Merry Riana pergi sendirian dengan bekal uang yang hanya cukup untuk beli makanan selama lima hari. Beruntungnya, ia bertemu dengan sahabatnya, Irene, yang ingin melanjutkan kuliah di universitas yang ada di sana juga. Dengan bantuan Irene, Merry bisa tinggal di asrama dan diterima di salah satu perguruan tinggi terbaik di sana. Tetapi, itu semua baru bisa dapat bila Merry membayar $40,000. Satu-satunya harapan adalah mengambil pinjaman mahasiswa, yang hanya bisa didapat jika Merry memiliki seorang penjamin. Kemudian, Merry bertemu dengan seniornya, Alva. Ia adalah orang yang sangat perhitungan. Ia memberi segala macam syarat sebelum akhirnya setuju untuk menolong Merry. Ia juga menyuruh Meery mencari kerja sambilan. Merry sadar bahwa ia harus sukses secepatnya. Segala macam pekerjaan ia kerjakan, mulai dari menyebar brosur bisnis online, sampai bermain saham beresiko tinggi. Kondisi ekonominya pun naik turun. Kemelut cinta pun terjadi ketika Alva menyatakan perasaan padanya, sementara Merry tahu betul bahwa Irene tengah jatuh cinta pada Alva. Interpretasi Akting Chelsea Islan Merry Riana dalam film ini sangat bagus. Ia mampu memainkan peran sebagai Merry Riana dengan sangat baik. Tetapi, film ini akan menjadi lebih bagus jika tidak ada adegan ciuman. Rangkuman Saya pikir ini adalah film yang inspiratif yang bisa memotivasi orang-orang untuk sukses di usia muda. Hal ini membawa semangat yang baik bagi pemuda-pemuda di Indonesia. Penulis skrip dalam film ini juga berhasil membawa seraingkaian konflik yang membuat jalan cerita menjadi lebih hidup. Example of Review Text – “Love You Like a Love Song” Selena Gomez “Love You Like a Love Song” is single from one of Disney’s shining stars, Selena Gomez. The young men or women who love this young singer/actress will like this song. Gomez isn’t known for having a super-strong voice or the most original arrangements, but she deserves props for this song, which mercifully tones down the standard synth-pop noise and kicks the vocal performance up a notch. The end result sounds a bit more creative and mature than the rest of the bubblegum-pop pack. Selena’s music is always great, and her voice sounds great especially in the bridge. In the past century people seem to believe that a love song for pop has to be acoustic with guitars, and love songs for Rap/Hip-Hop have to sound the same. This doesn’t seem to bother Rihanna, Lady GaGa, and now Selena Gomez. To be honest in the past century “Love You Like A Love Song” has been the most original love song in years. Monotune was perfectly done here, and the Autotune was good layered, Autotune is not just robotic Beyonce and Rihanna use it to. Must original love song and just song in years. Its about loving someone like a love song its gonna use love song cliches. Terjemahannya “Love You Like a Love Song” Selena Gomez “Love You Like a Love Song” adalah single dari salah satu bintang bersinar Disney, Selena Gomez. Anak-anak muda yang mencintai penyanyi / aktris muda ini akan menyukai lagu ini. Gomez tidak dikenal memiliki suara yang sangat kuat atau pengaturan yang paling orisinil, namun ia pantas menjadi pemeran untuk lagu ini, yang dengan nada penuh kasih menon-aktifkan suara synth-pop standar dan menendang kinerja vokal sampai takik. Hasil akhirnya terdengar sedikit lebih kreatif dan matang dibandingkan dengan paket bubblegum-pop lainnya. Musik Selena selalu bagus, dan suaranya terdengar hebat terutama di jembatan. Pada saat ini orang tampaknya percaya bahwa lagu cinta untuk pop harus akustik dengan gitar, dan lagu cinta untuk Rap / Hip-Hop harus terdengar sama. Sepertinya ini tidak diperdulikan Rihanna, Lady GaGa, dan sekarang Selena Gomez. Sejujurnya masa ini “Love You Like A Love Song” telah menjadi lagu cinta paling orisinil selama bertahun-tahun. Monotune sempurna dilakukan di sini, dan Autotune nya bagus dan berlapis, Autotune bukan hanya seperti robot ala Beyonce dan Rihanna yang menggunakannya. Harus lagu cinta orisinal dan nyanyikan lagu hanya dalam beberapa tahun. Its tentang mencintai seseorang seperti lagu cinta yang akan menggunakan lagu cinta klise. Related Articles Report Text ; Definition, Generic Structures, Purposes, Language Features That is the our explanation about Review Text. Hopefully by reading our explanation above you can get more understanding about this material. Okay, I think that’s all, thanks for your visit. If you have any questions or comments regarding this material please leave a comment . Reference Rudi Hartono, Genre of Texts, Semarang English Department Faculty of Language and Art Semarang State University, 2005. Mark Andersons and Kathy Andersons, Text Type in English 1-2, Australia MacMillanEducation, 2003. Terima kasih atas kunjungannya. Semoga dengan berkunjung di website British Course ini sobat bisa makin cinta bahasa inggris, dan nilai bahasa inggris sobat semakin memuaskan. Dan semoga kita bisa belajar bahasa inggris bareng dan saling mengenal. Komentar, saran dan kritik dari sobat kami harapkan demi kemajuan website ini. Thanks..

language features of review text