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Journal ArticleDOI

Aspect-based sentiment analysis of movie reviews on discussion boards

TLDR
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.
Abstract
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.

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Citations
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Proceedings ArticleDOI

SemEval-2014 Task 4: Aspect Based Sentiment Analysis

TL;DR: SemEval2014 Task 4 aimed to foster research in the field of aspect-based sentiment analysis, where the goal is to identify the aspects of given target entities and the sentiment expressed for each aspect.
Proceedings ArticleDOI

SemEval-2015 Task 12: Aspect Based Sentiment Analysis

TL;DR: The task provided manually annotated reviews in three domains (restaurants, laptops and hotels), and a common evaluation procedure, to foster research beyond sentenceor text-level sentiment classification towards Aspect Based Sentiment Analysis.
Journal ArticleDOI

More than words: Social networks' text mining for consumer brand sentiments

TL;DR: This study uses a random sample of 3516 tweets to evaluate consumers' sentiment towards well-known brands such as Nokia, T-Mobile, IBM, KLM and DHL and indicates a generally positive consumer sentiment towards several famous brands.
Journal ArticleDOI

Sentiment analysis

TL;DR: The goal of this work is to review and compare some free access web services, analyzing their capabilities to classify and score different pieces of text with respect to the sentiments contained therein.
References
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Book ChapterDOI

Text Categorization with Suport Vector Machines: Learning with Many Relevant Features

TL;DR: This paper explores the use of Support Vector Machines for learning text classifiers from examples and analyzes the particular properties of learning with text data and identifies why SVMs are appropriate for this task.
Proceedings ArticleDOI

Mining and summarizing customer reviews

TL;DR: This research aims to mine and to summarize all the customer reviews of a product, and proposes several novel techniques to perform these tasks.

Thumbs up? Sentiment Classiflcation using Machine Learning Techniques

TL;DR: In this paper, the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, was considered and three machine learning methods (Naive Bayes, maximum entropy classiflcation, and support vector machines) were employed.
Proceedings ArticleDOI

Thumbs up? Sentiment Classification using Machine Learning Techniques

TL;DR: This work considers the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, and concludes by examining factors that make the sentiment classification problem more challenging.
Book

A Comprehensive Grammar of the English Language

TL;DR: A Comprehensive grammar of the English language as mentioned in this paper, a comprehensive grammar of English language, a Comprehensive grammar for English language, and a comprehensive grammars of English, is an example of such a grammar.