Tun Thura Thet

Aspect-based sentiment analysis of movie reviews on discussion boards

In this article, a method for automatic sentiment analysis of movie reviews is proposed, implemented and evaluated. In contrast to most studies that focus on determining only sentiment orientation (positive versus negative), the proposed method performs fine-grained analysis to determine both the sentiment orientation and sentiment strength of the reviewer towards various aspects of a movie. Sentences in review documents contain independent clauses that express different sentiments toward different aspects of a movie. The method adopts a linguistic approach of computing the sentiment of a clause from the prior sentiment scores assigned to individual words, taking into consideration the grammatical dependency structure of the clause. The prior sentiment scores of about 32,000 individual words are derived from SentiWordNet with the help of a subjectivity lexicon. Negation is delicately handled. The output sentiment scores can be used to identify the most positive and negative clauses or sentences with respect to particular movie aspects.

Comparing sentiment expression in movie reviews from four online genres

Purpose – This paper aims to investigate the characteristics and differences in sentiment expression in movie review documents from four online opinion genres – blog postings, discussion board threads, user reviews, and critic reviews.Design/methodology/approach – A collection of movie review documents was harvested from the four types of web sources, and a sample of 520 movie reviews were analysed to compare the content and textual characteristics across the four genres. The analysis focused on document and sentence length, part‐of‐speech distribution, vocabulary, aspects of movies discussed, star ratings used and multimedia content in the reviews. The study also identified frequently occurring positive and negative terms in the different genres, as well as the pattern of responses in discussion threads.Findings – Critic reviews and blog postings are longer than user reviews and discussion threads, and contain longer sentences. Critic reviews and blogs contain more nouns and prepositions, whereas discuss...

Word segmentation for the Myanmar language

This study reports the development of a Myanmar word segmentation method using Unicode standard encoding. Word segmentation is an essential step prior to natural language processing in the Myanmar language, because a Myanmar text is a string of characters without explicit word boundary delimiters. The proposed method has two phases: syllable segmentation and syllable merging. A rule-based heuristic approach was adopted for syllable segmentation, and a dictionary-based statistical approach for syllable merging. Evaluation of test results showed that the method is very effective for the Myanmar language.

Sentiment Classification of Movie Reviews Using Multiple Perspectives

This study develops an automatic method for in-depth sentiment analysis of movie review documents using information extraction techniques and a machine learning approach. The analysis results provide sentiment orientations in multiple perspectives, each focusing on a specific aspect of the reviewed entity. Sentiment classification in multiple perspectives can provide more comprehensive sentiment analysis for applications like sentiment ranking and rating. By utilizing information extraction techniques such as entity extraction, co-referencing and pronoun resolution, the review texts are segmented into sections where each section discusses particular aspect of the reviewed entity. For each section of sentences, Support Vector Machine (SVM) using vectors of terms is applied to determine sentiment orientation toward the target aspect. In our exploratory study, we focus on the sentiment orientations toward overall movie, movie directors and casts in the movie. The experimental results prove the effectiveness of the proposed approach for sentiment classification of movie reviews.

Effectiveness of web search results for genre and sentiment classification

The motivation of this study is to enhance general topical search with a sentiment-based one where the search results (snippets) returned by the web search engine are clustered by sentiment categories. Firstly we developed an automatic method to identify product review documents using the snippets (summary information that includes the URL, title, and summary text), which is genre classification. Then the identified snippets were automatically classified into positive (recommended) and negative (non-recommended) documents, which is sentiment classification. Thereafter the user may directly decide to access the positive or negative review documents. In this study we used only the snippets rather than their original full-text documents, and applied a common machine learning technique, SVM (support vector machine), and heuristic approaches to investigate how effectively the snippets can be used for genre and sentiment classification. The results show that the web search engine should improve the quality of the snippets especially for opinionated documents (i.e. review documents).

Visual sentiment summarization of movie reviews

A prototype digital library of social media content was developed to present a summarized view of public opinion in a visual interface. The domain of the study was movie reviews of multiple genres harvested from weblogs, discussion boards, user and critic review Web sites, and Twitter. The system performs fine-grained analysis to determine both the sentiment orientation and sentiment strength of the reviewer towards various aspects of a movie, such as overall opinion, director, cast, story, scene, and music. Various visual interface components were developed to present an overview of public opinion on multiple aspects of each movie, and a usability evaluation was conducted to observe their effectiveness. Aspect-based sentiment summarization interface has the highest score for usefulness while a sentiment link analysis graph visualizing how positive and negative sentiment terms are associated with review aspects has the highest score for overall rating.

Automatic classification of web search results: product review vs. non-review documents

This study seeks to develop an automatic method to identify product review documents on the Web using the snippets (summary information that includes the URL, title, and summary text) returned by the Web search engine. The aim is to allow the user to extend topical search with genre-based filtering or categorization. Firstly we applied a common machine learning technique, SVM (Support Vector Machine), to investigate which features of the snippets are useful for classification. The best results were obtained using just the title and URL (domain and folder names) of the snippets as phrase terms (n-grams). Then we developed a heuristic approach that utilizes domain knowledge constructed semi-automatically, and found that it performs comparatively well, with only a small drop in accuracy rates. A hybrid approach which combines both the machine learning and heuristic approaches performs slightly better than the machine learning approach alone.

Filtering product reviews from web search results

This study seeks to develop an automatic method to identify product reviews on the Web using the snippets (summary information) returned by search engines. Determining whether a snippet is a review or non-review is a challenging task, since the snippet usually does not contain many useful features for identifying review documents. Firstly we applied a common machine learning technique, SVM (Support Vector Machine), to investigate which features of snippets are useful for the classification. Then we employed a heuristic approach utilizing domain knowledge and found that the heuristic approach performs equally well as the machine learning approach. A hybrid approach which combines the machine learning technique and domain knowledge performs slightly better than the machine learning approach alone.

A Sentiment-Based Digital Library of Movie Review Documents Using Fedora / Une bibliothèque numérique de documents critiques de films basée sur les sentiments en utilisant Fedora

This study develops a digital library of movie review documents that supports sentiment-based browsing and searching. Firstly, we develop an automatic method for in-depth sentiment analysis and classification of movie review documents to provide sentiment orientations toward multiple perspectives of movies, such as overall opinion about the movie, director, and cast. By utilizing information extraction techniques such as entity extraction, co-referencing, and pronoun resolution, the review texts are segmented into multiple sections where each section contains multiple sentences and discusses a particular aspect of the reviewed movie. For each aspect section, a machine-learning algorithm, Support Vector Machine (SVM), is applied to determine sentiment orientation toward the target aspect. Secondly a prototype digital library is developed with the automatically analysed data to show the usefulness of sentiment-based browsing and searching. Using the system, the user can browse and search movies by sentiment polarity (positive, neutral, or negative) of multiple aspects in the movie. Finally, a usability evaluation is conducted to observe the effectiveness of the sentiment-based digital library. Cette étude examine le développement d’une bibliothèque numérique de documents critiques de films permettant l’exploration et la recherche par sentiments. Pour commencer, nous développons une méthode automatique pour l’analyse en profondeur des sentiments et la classification des documents critiques de films propres à fournir des orientations à propos des sentiments capables d’offrir des perspectives multiples sur les films, comme par exemple l’opinion générale sur le film, sur le metteur en scène, et sur les acteurs. Grâce à l’utilisation de techniques d’extraction d’information telles que l’extraction d’entités, le co-référencement, et la résolution de pronoms, les comptes rendus sont segmentés en de multiples sections où chacune contient plusieurs phrases et aborde un aspect particulier du film en question. À chacune de ces sections on applique un algorithme d’apprentissage automatique, Support Vector Machine (SVM), qui détermine l’orientation du ou des sentiments pour cette section. Ensuite, nous développons un prototype de bibliothèque numérique en utilisant les données analysées automatiquement afin de montrer l’utilité de l’exploration et de la recherche par sentiments. En utilisant ce système, l’utilisateur peut explorer et faire des recherches dans les films selon les polarités des sentiments (positif, neutre, ou négatif) et ce, sur de nombreux aspects des films. Pour finir, nous avons effectué une évaluation d’utilisabilité afin de vérifier l’efficacité d’une bibliothèque numérique basée sur les sentiments.