Using sentiment analysis to predict Amazon ratings : a comparative study using dictionaries approaches

This dissertation delves into the domain of sentiment analysis, a computational approach to detect and extract human sentiments from textual data. With the ever-increasing growth of online textual content, especially in the form of reviews, the need to accurately determine customer sentiment has never been more imperative. To explore the efficacy of lexicon-based sentiment analysis models, this study implements 9 models: VADER, TextBlob, NRC Lexicon, SentiWordNet, Pattern, AFINN, Opinion Lexicon, LabMT, and ANEW. These models are tested on an Amazon reviews dataset, which is uniquely accompanied by a rating system in which the accuracy of the sentiment extraction can be assessed. The study then further delves into a comparative analysis, collecting the performance of these models to discern their strengths, weaknesses, and overall utility.Esta dissertação aborda o tema de Sentiment Analysis, uma técnica que permite detetar e extrair sentimentos humanos a partir de texto. Com o crescimento exponencial de dados sob a forma de texto online, particularmente nas avaliações dos consumidores, a necessidade de determinar com precisão os sentimentos destes nunca foi tão imperativo. Esta técnica é essencial para converter os dados textuais em informação que pode ser efetivamente utilizada. Para explorar a eficácia dos modelos de Sentiment Analysis na categoria de abordagem por Dicionário, este estudo implementa nove modelos: VADER, TextBlob, NRC Lexicon, SentiWordNet, Pattern, AFINN, Opinion Lexicon, LabMT e ANEW. Estes modelos são testados numa base de dados que contém avaliações da Amazon e classificações através das quais a precisão da extração de sentimento pode ser avaliada. O estudo aprofunda-se numa análise comparativa, avaliando o desempenho destes modelos para identificar os seus pontos fortes, fracos e a sua utilidade

Subjectivity Analysis In Opinion Mining - A Systematic Literature Review

Subjectivity analysis determines existence of subjectivity in text using subjective clues.It is the first task in opinion mining process.The difference between subjectivity analysis and polarity determination is the latter process subjective text to determine the orientation as positive or negative.There were many techniques used to solve the problem of segregating subjective and objective text.This paper used systematic literature review (SLR) to compile the undertaking study in subjective analysis.SLR is a literature review that collects multiple and critically analyse multiple studies to answer the research questions.Eight research questions were drawn for this purpose.Information such as technique,corpus,subjective clues representation and performance were extracted from 97 articles known as primary studies.This information was analysed to identify the strengths and weaknesses of the technique,affecting elements to the performance and missing elements from the subjectivity analysis.The SLR has found that majority of the study are using machine learning approach to identify and learn subjective text due to the nature of subjectivity analysis problem that is viewed as classification problem.The performance of this approach outperformed other approaches though currently it is at satisfactory level.Therefore,more studies are needed to improve the performance of subjectivity analysis

Tipping the scales: exploring the added value of deep semantic processing on readability prediction and sentiment analysis

Applications which make use of natural language processing (NLP) are said to benefit more from incorporating a rich model of text meaning than from a basic representation in the form of bag-of-words. This thesis set out to explore the added value of incorporating deep semantic information in two end-user applications that normally rely mostly on superficial and lexical information, viz. readability prediction and aspect-based sentiment analysis. For both applications we apply supervised machine learning techniques and focus on the incorporation of coreference and semantic role information. To this purpose, we adapted a Dutch coreference resolution system and developed a semantic role labeler for Dutch. We tested the cross-genre robustness of both systems and in a next phase retrained them on a large corpus comprising a variety of text genres. For the readability prediction task, we first built a general-purpose corpus consisting of a large variety of text genres which was then assessed on readability. Moreover, we proposed an assessment technique which has not previously been used in readability assessment, namely crowdsourcing, and revealed that crowdsourcing is a viable alternative to the more traditional assessment technique of having experts assign labels. We built the first state-of-the-art classification-based readability prediction system relying on a rich feature space of traditional, lexical, syntactic and shallow semantic features. Furthermore, we enriched this tool by introducing new features based on coreference resolution and semantic role labeling. We then explored the added value of incorporating this deep semantic information by performing two different rounds of experiments. In the first round these features were manually in- or excluded and in the second round joint optimization experiments were performed using a wrapper-based feature selection system based on genetic algorithms. In both setups, we investigated whether there was a difference in performance when these features were derived from gold standard information compared to when they were automatically generated, which allowed us to assess the true upper bound of incorporating this type of information. Our results revealed that readability classification definitely benefits from the incorporation of semantic information in the form of coreference and semantic role features. More precisely, we found that the best results for both tasks were achieved after jointly optimizing the hyperparameters and semantic features using genetic algorithms. Contrary to our expectations, we observed that our system achieved its best performance when relying on the automatically predicted deep semantic features. This is an interesting result, as our ultimate goal is to predict readability based exclusively on automatically-derived information sources. For the aspect-based sentiment analysis task, we developed the first Dutch end-to-end system. We therefore collected a corpus of Dutch restaurant reviews and annotated each review with aspect term expressions and polarity. For the creation of our system, we distinguished three individual subtasks: aspect term extraction, aspect category classification and aspect polarity classification. We then investigated the added value of our two semantic information layers in the second subtask of aspect category classification. In a first setup, we focussed on investigating the added value of performing coreference resolution prior to classification in order to derive which implicit aspect terms (anaphors) could be linked to which explicit aspect terms (antecedents). In these experiments, we explored how the performance of a baseline classifier relying on lexical information alone would benefit from additional semantic information in the form of lexical-semantic and semantic role features. We hypothesized that if coreference resolution was performed prior to classification, more of this semantic information could be derived, i.e. for the implicit aspect terms, which would result in a better performance. In this respect, we optimized our classifier using a wrapper-based approach for feature selection and we compared a setting where we relied on gold-standard anaphor-antecedent pairs to a setting where these had been predicted. Our results revealed a very moderate performance gain and underlined that incorporating coreference information only proves useful when integrating gold-standard coreference annotations. When coreference relations were derived automatically, this led to an overall decrease in performance because of semantic mismatches. When comparing the semantic role to the lexical-semantic features, it seemed that especially the latter features allow for a better performance. In a second setup, we investigated how to resolve implicit aspect terms. We compared a setting where gold-standard coreference resolution was used for this purpose to a setting where the implicit aspects were derived from a simple subjectivity heuristic. Our results revealed that using this heuristic results in a better coverage and performance, which means that, overall, it was difficult to find an added value in resolving coreference first. Does deep semantic information help tip the scales on performance? For Dutch readability prediction, we found that it does, when integrated in a state-of-the-art classifier. By using such information for Dutch aspect-based sentiment analysis, we found that this approach adds weight to the scales, but cannot make them tip

Modality and learner academic writing across genres:an analysis of discourse, socialisation and teacher cognition on a 20-week pre-sessional programme

Modality is a complex yet pervasive feature of the English language which is typically difficult for non-native speakers of English to acquire. It is even more so for learners of English who wish to undertake advanced academic study in an English-speaking context, as it requires knowledge of both discipline and genre specific norms and to be able to adapt to reader expectations. This study uses a mixed methods design to analyse the longitudinal development of modality in learner academic writing on a 20-week pre-sessional programme at a UK university. The research triangulates the findings obtained from the analysis of three distinct datasets in order to identify the factors involved in influencing amateur writer output in their assessed written texts. The main focus of the study is an in-depth discourse analysis contrasting expert (successful Masters students) and amateur (pre-sessional) writing in three genres of academic writing within the discipline of Business and Economics. A functional approach is adopted to analyse the expression of modality. This is complemented by an analysis of the teaching material used on the programme and combined with insights on teacher cognition from a series of interviews. The findings show a development in interlanguage, with movement to closer alignment in modal expressions between the types of writers as the programme progresses. However, the findings also show that modality is marginalised as a language item in the teaching materials, in the assessment task types and in the marking criteria, with preference given to rhetorical structures within texts. Tutors also report varying degrees of comfort, expertise and familiarity with regards to modality. The research concludes by making a series of pedagogical recommendations in order to re-direct some of the attention in academic writing instruction back to modality and to integrate it more explicitly and appropriately within the course design

VOICE IN ESL ACADEMIC WRITING: AN INTERPERSONAL ANALYSIS

Social relationships determine every linguistic choice people make, regardless of the medium of language use. Hence, it is important to understand how these social relationships determine the linguistic features that are necessary for creating a proper voice when writing academically. This study uses the framework of Systemic Functional Linguistics in an attempt to understand intermediate English as a Second Language (ESL) learners’ use of interpersonal features to create a voice in their academic writing and to see if it aligns with the voice typical of Western academic writing. In order to do this, the study uses twenty-four writing samples from eight participants (3 essays per participant) of varying native languages. Using the system of MOOD, the writing samples are analyzed for three specific interpersonal linguistic features: Subject, Adjunct, and Finite, to determine the amount of authority, objectivity, and abstractness the participants create in their writing. Finding that the participants were unable to create a voice consistent with Western academic writing, this study suggests some changes to current ESL pedagogical practices, in order to better prepare students academic study at the university level

Language on music : Beethoven, Mann and the absolute

Bibliography: 190-197.This dissertation investigates the general use of language on instrumental music. Three types of linguistic usage are identified: the metamusical, the systemic, and the metasystemic. In the first section, various forms of the metamusical - description, attempts at "recreation" and formal analyses of music - are considered, and are all shown to fail in different ways. The limitations of existing systems for negotiation between language and music are also brought to the fore. Failure is redefined, and shown to be intrinsically related to the tradition of musical ineffability, which finds its most extreme development in the notion of "absolute music". The second section attempts to provide a systemic discourse which takes the failure of language into account. Drawing on Lacan's imaginary/symbolic distinction and on Derrida's notion of the frame, it sets forth a construct called "the word of music", which is itself an impossible point of aspiration, but which manages to account for some of the dialectical complexities involved in systemic negotiation with a non-denotative form such as music. The third section entails metasystemic analysis proper; in other words, metamusical and systemic sources are analyzed and assessed. This part consists of a passage-by-passage translation of eight pages from Thomas Mann's Doctor Faustus, in which the fictional character Wendell Kretschmar delivers a lecture on and performance of Beethoven's Opus 111. Various metamusical and systemic issues are discussed: it is shown that Mann draws on a large number of established musicoliterary traditions, with his sources ranging from early Beethoven biographies to the writings of Theodor Adorno. Particular attention is given to the Romantic "Beethoven myth" and to Adorno's analysis of the composer's late music. Mann's negotiation between two partly opposing trends in the presentation of Opus 111 as an "ultimate" or "absolute" composition - the one based in a Romantic discourse of musical transcendence and the other originating in Adorno's identification of a tendency towards alienation in Beethoven's late style - is extensively discussed

A text-linguistic approach to shifts in the translation of ideologically oriented texts from English to Arabic

Abstract unavailable please refer to PD

Ideology through modality

This study is broadly concerned with the analysis of ideology in discourse. More specifically, it investigates the role modality plays in reflecting underlying ideologies as well as ideological inconsistencies in three practical analyses of discourse. Achieving these objectives is, I argue, dependent on a view of discourse which is not only functional but also pragmatic. The functional aspect of this view reflects the broad objectives of functional linguistics: i.e. relating linguistic structures to social structures. The pragmatic aspect reflects an emphasis on the need not to exclude 'the reader' from the process of interpretation. Whereas previous studies have either entirely neglected or presented an unsatisfactory account of the reader, the proposed functional-pragmatic approach to discourse analysis resolves this issue by allowing a systematic variance in interpretation. This is done in the light of a systematic account of modality which helps present a realistic and practical consideration of the role of the reader in approaching discourse analysis. Again, in line with a functional and pragmatic view of discourse, the argument put forward in this study is that all 'types' of discourse can be approached in a similar manner for critical analysis. Consequently, practical analyses of ideology through modality in three instances of discourse: literary texts, political texts and scientific texts are presented. The overall aim is to show how a systematic, functional and pragmatic analysis of modality is adequate in critically analysing the ideologies present in all texts