Languageand culture. It has been seen that language is much more than the external expression and communication of internal thoughts formulated independently of their verbalization. In demonstrating the inadequacy and inappropriateness of such a view of language, attention has already been drawn to the ways in which one's native language is Persuasivetexts are evident in advertisements, debates, influential essays and articles (ACARA, 2014). Language features are a defining characteristic of a text. Each one of the aforementioned text types has its own specific language which is used to help convey the messages, themes and primary goals of the text. When constructing text types Creatingtexts; EN2-2A. Plans, composes and reviews a range of texts that are more demanding in terms of topic, audience and language English; Stage 2; A. Communicate through speaking, listening, reading, writing, viewing and representing; EN3-2A. Composes, edits and presents well-structured and coherent texts English; Stage 3 THELANGUAGE SPACE. Reading&Research; The Ultimate Research Writing Tool; TEXT TYPES FOR ENGLISH B You will need to learn to be able to use a variety of text features effectively when you produce the text types listed below. Knowing how to write these different text types is important in Paper Two, the writing paper, and also when you Previewthe latest features, enhancements, app updates, and more in iOS 16 for iPhone. New languages for Live Text Live Text adds recognition of Japanese, Korean, and Ukrainian text. when you add a new medication you'll receive an alert if there is a critical interaction. You can review critical, serious, and moderate interactions in Sharethis. Have you found the page useful? Please use the following to spread the word: APA All Acronyms. 2022. LFTT - Language Features of Text Types. IBDP Language & Literature. IB DP Language & Literature involves the study and analysis of fiction texts of varying genres (6 at HL and 4 for SL), as well as countless short fiction, non-fiction, media, and visual texts. Students will focus closely on the language of the texts they study and become aware of each text's purpose and wider Markuplanguage is a system for formatting and arranging the elements in a document using tags. Unlike physical annotations and markups on paper documents, these tags only appear in the document while the author is writing the text. When an application processes the markup, the content will simply appear as formatted text to the viewer. zwvw4G. In linguistics, the term text refers to The original words of something written, printed, or spoken, in contrast to a summary or paraphrase. A coherent stretch of language that may be regarded as an object of critical analysis. Text linguistics refers to a form of discourse analysis—a method of studying written or spoken language—that is concerned with the description and analysis of extended texts those beyond the level of the single sentence. A text can be any example of written or spoken language, from something as complex as a book or legal document to something as simple as the body of an email or the words on the back of a cereal box. In the humanities, different fields of study concern themselves with different forms of texts. Literary theorists, for example, focus primarily on literary texts—novels, essays, stories, and poems. Legal scholars focus on legal texts such as laws, contracts, decrees, and regulations. Cultural theorists work with a wide variety of texts, including those that may not typically be the subject of studies, such as advertisements, signage, instruction manuals, and other ephemera. Text Definition Traditionally, a text is understood to be a piece of written or spoken material in its primary form as opposed to a paraphrase or summary. A text 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 referring to the words in the book, not the physical book itself. Information related to a text, and often printed alongside it—such as an author's name, the publisher, the date of publication, etc.—is known as paratext. The idea of what constitutes a text has evolved over time. In recent years, the dynamics of technology—especially social media—have expanded the notion of the text to include symbols such as emoticons and emojis. A sociologist studying teenage communication, for example, might refer to texts that combine traditional language and graphic symbols. Texts and New Technologies The concept of the text is not a stable one. It is always changing as the technologies for publishing and disseminating texts evolve. In the past, texts were usually presented as printed matter in bound volumes such as pamphlets or books. Today, however, people are more likely to encounter texts in digital space, where the materials are becoming "more fluid," according to linguists David Barton and Carmen Lee " Texts can no longer be thought of as relatively fixed and stable. They are more fluid with the changing affordances of new media. In addition, they are becoming increasingly multimodal and interactive. Links between texts are complex online, and intertextuality is common in online texts as people draw upon and play with other texts available on the web." An example of such intertextuality can be found in any popular news story. An article in The New York Times, for example, may contain embedded tweets from Twitter, links to outside articles, or links to primary sources such as press releases or other documents. With a text such as this, it is sometimes difficult to describe what exactly is part of the text and what is not. An embedded tweet, for instance, may be essential to understanding the text around it—and therefore part of the text itself—but it is also its own independent text. On social media sites such as Facebook and Twitter, as well as blogs and Wikipedia, it is common to find such relationships between texts. Text linguistics is a field of study where texts are treated as communication systems. The analysis deals with stretches of language beyond the single sentence and focuses particularly on context, information that goes along with what is said and written. Context includes such things as the social relationship between two speakers or correspondents, the place where communication occurs, and non-verbal information such as body language. Linguists use this contextual information to describe the "socio-cultural environment" in which a text exists. Sources Barton, David, and Carmen Lee. "Language Online Investigating Digital Texts and Practices." Routledge, Ronald, and Michael McCarthy. "Cambridge Grammar of English." Cambridge University Press, Marvin K. L., et al. "Linguistic Perspectives on Literature." Routledge, 2015. AbstractOnline reviews play a critical role in customer’s purchase decision making process on the web. The reviews are often ranked based on user helpfulness votes to minimize the review information overload problem. This paper examines the factors that contribute towards helpfulness of online reviews and builds a predictive model. The proposed predictive model extracts novel linguistic category features by analysing the textual content of reviews. In addition, the model makes use of review metadata, subjectivity and readability related features for helpfulness prediction. Our experimental analysis on two real-life review datasets reveals that a hybrid set of features deliver the best predictive accuracy. We also show that the proposed linguistic category features are better predictors of review helpfulness for experience goods such as books, music, and video games. The findings of this study can provide new insights to e-commerce retailers for better organization and ranking of online reviews and help customers in making better product advent of Web has enabled users to share their opinions, experiences and knowledge via blogs, forums, and other social media websites. In the e-commerce context, Web allows consumers to share their purchase and usage experiences in the form of product reviews Amazon product reviews, CNET reviews. Such reviews contain valuable information and are often used by potential customers for making purchase decisions. However, some of the most popular products receive several hundreds or thousands of reviews resulting in a review information overload problem. Besides, the review quality across large volume of reviews exhibits wide variations Liu et al., 2008, Tsur and Rappoport, 2009.In order to help potential customers in navigating through large volume of reviews, e-commerce websites provide an interactive voting feature. For example, Amazon asks its review viewers “Was this review helpful? Yes/No” to get user votes on reviews. The votes thus gathered from multiple users are then aggregated, ranked and presented, “24 of 36 people found the following review helpful”. Reviews with higher share of helpful votes are ranked higher than the ones with lower helpful votes. This paper aims to study the factors that play an important role for a review to get higher helpful votes. Such an analysis is important for the following reasons First, reviews can be effectively summarized by filtering low quality reviews. Second, websites that do not use voting feature could benefit from an automated helpfulness prediction system. Third, review ranking system could be improved with a better understanding of the underlying review helpfulness factors, avoiding early bird bias problem Liu, Cao, Lin, Huang, & Zhou, 2007.The review voting behaviour which influences review helpfulness can be visualized as a socio-psychological process between the reviewer and the reviewee. This process is facilitated by Web as a communication medium. Language plays a very important role in this process between the reviewer and reviewee. In an offline world, communication between a sender and receiver is often influenced by non-verbal cues, communication contexts and past interactions between the sender and receiver. In the absence of such external factors in the online world, language plays a crucial role. The sender’s message composed using a language impacts the receivers cognition and influences their behaviour. As the sender’s message can be composed in numerous ways, its impact on the receivers cognition and behaviour varies. Our basic intuition is that the review voting behaviour can be better understood by studying the psychological properties and propensities of the language. The Linguistic Category Model LCM proposed by Semin and Fiedler 1991 is a conceptual framework that models psychological properties of the language. The linguistic categories used in the LCM model and their descriptions are presented in Table LCM model Coenen et al., 2006, Semin and Fiedler, 1991 uses three broad linguistic categories, namely Adjectives fantastic, excellent, beautiful, State verbs love, hate, envy and Action verbs. The action verbs are further sub-divided into State Action Verbs amaze, anger, shock, Interpretive Action Verbs help, avoid, recommend, and Descriptive Action Verbs call, talk, run. All of these linguistic categories are organized on a abstract-to-concrete dimension. At one extreme ADJ the terms are abstract, less verifiable, more disputable and least informative. While at the other extreme DAVs, the terms are concrete, verifiable, less disputable and most the following three review examples tagged with key linguistic categories fantastic ADJ camera. The picture quality of this camera is wonderful ADJ. is my first camera and I love SV it. The camera is excellent ADJ. regularly takeDAV pics with this camera. The quality of the pics has really amazed SAV me. Battery life is fabulous ADJ. My only issue is that it makes DAV a lot of noise in autofocus mode. I strongly recommend IAV this 1 is highly abstract and subjective as it primarily uses adjectives. Review 2 uses a subjective verb love’ indicating the emotional state of the reviewer. The last review provides a more concrete and objective description of the camera using DAVs. Besides, it also contains subjective ADJ opinion of the reviewer. It is evident that the review 3 with far more concrete and descriptive information is likely to be more helpful than other two reviews for purchase decision making. Therefore, our basic intuition is that the linguistic categories impact the receivers or consumers cognitive process, influence their voting behaviour and affect review this paper, our objective is to examine the use of such linguistic category features for predicting review helpfulness. We make a first attempt at devising a new method for extracting linguistic category features from review text and build a binary classification model. We conduct a detailed experimental analysis on two real-life review datasets to demonstrate the utility of the proposed linguistic features. Furthermore, we study the effect of product type on review helpfulness and show that the proposed linguistic features are better predictors of review helpfulness for experience rest of the paper is organized as follows. Section 2 describes the related work on review helpfulness. Section 3 elucidates the proposed novel features used in the model. Subsequently, Section 4 presents detailed experimental analysis, results and discussions. Section 5 highlights the implications of this research to theory and practice. Finally, Section 6 provides concluding remarks and outlines directions for future research snippetsRelated literatureZhang and Varadarajan 2006 build a regression model for predicting the utility of product reviews. They use lexical similarity, syntactic terms based on Part-Of-Speech POS, and lexical subjectivity as features. Mudambi and Schuff 2010 formulated a linear regression model for determining factors that contribute towards review helpfulness. Their work was replicated by Huang and Yen 2013 and achieved just 15% explanatory power. The authors conclude that the review helpfulness predictionReview helpfulness modelWe first describe the terminology used in this paper and formally define the problem. Then, we explain the features used in our prediction review datasetsWe used two real-life datasets for the experimentation. First dataset is a publicly available multi-domain sentiment analysis dataset Blitzer, Dredze, & Pereira, 2007. This dataset has 13120 customer reviews across four different product categories. The second dataset, a more recent review dataset, is obtained by crawling website. The details of both the datasets are summarized in Table datasets are cleaned and prepared for analysis by applying the following threeImplicationsThe findings of this paper has implications for both theory and practice. From a theoretical perspective, the paper brings fresh ideas into the expert and intelligent systems research community from social psychology literature. The basic ideas for the linguistic category features introduced in this paper are borrowed from the LCM model Semin & Fiedler, 1991 used in psychology literature. Another important contribution of this paper is the design of automatic linguistic category featureConclusionsThis paper examined the online review helpfulness problem and built a new prediction model. The proposed model used hybrid set of features review metadata, subjectivity, readability, and linguistic category to predict review helpfulness. The effectiveness of the proposed model was empirically evaluated on two real-life review datasets. The linguistic category features was found to be effective in predicting helpfulness of experience paper described an automatic linguistic categoryReferences 30 et determinants of voting for the helpfulness of online user reviews A text mining approachDecision Support Systems2011N. Korfiatis et content quality and helpfulness of online product reviews The interplay of review helpfulness vs. review contentElectronic Commerce Research and Applications2012S. Lee et the helpfulness of online reviews using multilayer perceptron neural networksExpert Systems with Applications2014Z. Liu et makes a useful online review? Implication for travel product websitesTourism Management2015 Ngo-Ye et influence of reviewer engagement characteristics on online review helpfulness A text regression modelDecision Support Systems2014Y. Pan et unequal A study of the helpfulness of user-generated product reviewsJournal of Retailing2011S. Baccianella et An enhanced lexical resource for sentiment analysis and opinion miningBird, S. 2006. Nltk The natural language toolkit. In Proceedings of the COLING/ACL on interactive presentation...Blitzer, J., Dredze, M., & Pereira, F. 2007. Biographies, bollywood, boomboxes and blenders Domain adaptation for...L. BreimanRandom forestsMachine Learning2001 Chang et A library for support vector machinesThe ACM Transactions on Interactive Intelligent Systems2011 Chua et review helpfulness as a function of reviewer reputation, review rating, and review depthJournal of the Association for Information Science and Technology2014Coenen, L. H. M., Hedebouw, L., & Semin, G. R. 2006. The Linguistic Category Model LCM. Retrieved from...DuBay, W. H. 2004. The principles of readability. Impact Information....A. Ghose et the helpfulness and economic impact of product reviews Mining text and reviewer characteristicsIEEE Transactions on Knowledge and Data Engineering2011Cited by 151Complementary or Substitutive? A Novel Deep Learning Method to Leverage Text-image Interactions for Multimodal Review Helpfulness Prediction2022, Expert Systems with ApplicationsSpecifically, the review-related features are exemplified by review sentiment extremity Li, Wu, & Mai, 2019, review timeliness Liu et al., 2008, review length Hong et al., 2017, writing style Siering, Muntermann, & Rajagopalan, 2018, etc. The textual semantic features of reviews such as multilingual characteristics Zhang & Lin, 2018, linguistic features Krishnamoorthy, 2015 were also verified as being of great importance to the RHP. To better leverage textual review information, researchers also adopted deep learning models to obtain powerful hidden representation features of the review texts Kong et al., 2020; Chen et al., 2018.View all citing articles on ScopusRecommended articles 6View full textCopyright © 2015 Elsevier Ltd. All rights reserved. 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..