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Combining strengths, emotions and polarities for boosting Twitter sentiment analysis
- Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining (p. 2). ACM
, 2013
"... Twitter sentiment analysis or the task of automatically retrieving opinions from tweets has received an increasing interest from the web mining community. This is due to its importance in a wide range of fields such as business and politics. People express senti-ments about specific topics or entiti ..."
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Twitter sentiment analysis or the task of automatically retrieving opinions from tweets has received an increasing interest from the web mining community. This is due to its importance in a wide range of fields such as business and politics. People express senti-ments about specific topics or entities with different strengths and intensities, where these sentiments are strongly related to their per-sonal feelings and emotions. A number of methods and lexical re-sources have been proposed to analyze sentiment from natural lan-guage texts, addressing different opinion dimensions. In this arti-cle, we propose an approach for boosting Twitter sentiment classifi-cation using different sentiment dimensions as meta-level features. We combine aspects such as opinion strength, emotion and polarity indicators, generated by existing sentiment analysis methods and resources. Our research shows that the combination of sentiment dimensions provides significant improvement in Twitter sentiment classification tasks such as polarity and subjectivity.
Sentiment Analysis in Twitter with Lightweight Discourse Analysis ABSTRACT
"... We propose a lightweight method for using discourse relations for polarity detection of tweets. This method is targeted towards the web-based applications that deal with noisy, unstructured text, like the tweets, and cannot afford to use heavy linguistic resources like parsing due to frequent failur ..."
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We propose a lightweight method for using discourse relations for polarity detection of tweets. This method is targeted towards the web-based applications that deal with noisy, unstructured text, like the tweets, and cannot afford to use heavy linguistic resources like parsing due to frequent failure of the parsers to handle noisy data. Most of the works in micro-blogs, like Twitter, use a bag-of-words model that ignores the discourse particles like but, since, although etc. In this work, we show how the discourse relations like the connectives and conditionals can be used to incorporate discourse information in any bag-of-words model, to improve sentiment classification accuracy. We also probe the influence of the semantic operators like modals and negations on the discourse relations that affect the sentiment of a sentence. Discourse relations and corresponding rules are identified with minimal processing- just a list look up. We first give a linguistic description of the various discourse relations which leads to conditions in rules and features in SVM. We show that our discourse-based bag-of-words model performs well in a noisy medium (Twitter), where it performs better than an existing Twitter-based application. Furthermore, we show that our approach is beneficial to structured reviews as well, where we achieve a better accuracy than a state-of-the-art system in the travel review domain. Our system compares favorably with the state-of-the-art systems and has the additional attractiveness of being less resource intensive.
Sentiment-based ranking of blog posts using rhetorical structure theory
, 2013
"... Abstract. Polarity estimation in large-scale and multi-topic domains is a difficult issue. Most state-of-the-art solutions essentially rely on fre-quencies of sentiment-carrying words (e.g., taken from a lexicon) when analyzing the sentiment conveyed by natural language text. These ap-proaches ignor ..."
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Abstract. Polarity estimation in large-scale and multi-topic domains is a difficult issue. Most state-of-the-art solutions essentially rely on fre-quencies of sentiment-carrying words (e.g., taken from a lexicon) when analyzing the sentiment conveyed by natural language text. These ap-proaches ignore the structural aspects of a document, which contain valu-able information. Rhetorical Structure Theory (RST) provides important information about the relative importance of the different text spans in a document. This knowledge could be useful for sentiment analysis and polarity classification. However, RST has only been studied for polar-ity classification problems in constrained and small scale scenarios. The main objective of this paper is to explore the usefulness of RST in large-scale polarity ranking of blog posts. We apply sentence-level methods to select the key sentences that convey the overall on-topic sentiment of a blog post. Then, we apply RST analysis to these core sentences in order to guide the classification of their polarity and thus to generate an overall estimation of the document’s polarity with respect to a specific topic. Our results show that RST provides valuable information about the discourse structure of the texts that can be used to make a more accurate ranking of documents in terms of their estimated sentiment in multi-topic blogs.
A Review Corpus for Argumentation Analysis
- In Proceedings of the 15th International Conference on Intelligent Text Processing and Computational Linguistics
, 2014
"... Abstract. The analysis of user reviews has become critical in research and industry, as user reviews increasingly impact the reputation of prod-ucts and services. Many review texts comprise an involved argumentation with facts and opinions on different product features or aspects. There-fore, classi ..."
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Abstract. The analysis of user reviews has become critical in research and industry, as user reviews increasingly impact the reputation of prod-ucts and services. Many review texts comprise an involved argumentation with facts and opinions on different product features or aspects. There-fore, classifying sentiment polarity does not suffice to capture a review’s impact. We claim that an argumentation analysis is needed, including opinion summarization, sentiment score prediction, and others. Since ex-isting language resources to drive such research are missing, we have de-signed the ArguAna TripAdvisor corpus, which compiles 2,100 manually annotated hotel reviews balanced with respect to the reviews ’ sentiment scores. Each review text is segmented into facts, positive, and negative opinions, while all hotel aspects and amenities are marked. In this paper, we present the design and a first study of the corpus. We reveal patterns of local sentiment that correlate with sentiment scores, thereby defining a promising starting point for an effective argumentation analysis. 1
A Machine Learning Approach for Subjectivity Classification Based on
- Positional and Discourse Features, in: Multidisciplinary Information Retrieval
"... Abstract. In recent years, several machine learning methods have been proposed to detect subjective (opinionated) expressions within on-line documents. This task is important in many Opinion Mining and Senti-ment Analysis applications. However, the opinion extraction process is often done with rough ..."
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Abstract. In recent years, several machine learning methods have been proposed to detect subjective (opinionated) expressions within on-line documents. This task is important in many Opinion Mining and Senti-ment Analysis applications. However, the opinion extraction process is often done with rough content-based features. In this paper, we study the role of structural features to guide sentence-level subjectivity clas-sification. More specifically, we combine classical n-grams features with novel features defined from positional information and from the discourse structure of the sentences. Our experiments show that these new features are beneficial in the classification of subjective sentences.
Aspect-Oriented Sentiment Analysis of Customer Reviews Using Distant Supervision Techniques
, 2013
"... The opinions and experiences of other people constitute an important source of information in our everyday life. For example, we ask our friends which dentist, restaurant, or smartphone they would recommend to us. Nowadays, online customer reviews have become an invaluable resource to answer such q ..."
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The opinions and experiences of other people constitute an important source of information in our everyday life. For example, we ask our friends which dentist, restaurant, or smartphone they would recommend to us. Nowadays, online customer reviews have become an invaluable resource to answer such questions. Besides helping consumers to make more informed purchase decisions, online reviews are also of great value to vendors, as they represent unsolicited and genuine customer feedback that is conveniently available at virtually no costs. However, for popular products there often exist several thousands of reviews so that manual analysis is not an option. In this thesis, we provide a comprehensive study of how to model and automatically analyze the opinion-rich information contained in customer reviews. In particular, we consider the task of aspect-oriented sentiment analysis. Given a collection of review texts, the task’s goal is to detect the individual product aspects reviewers have commented on and to decide whether the comments are rather positive or negative. Developing text analysis systems often involves the tedious and costly work of creating appropriate resources — for instance, labeling training corpora for machine learning methods
Meta-Level Sentiment Models for Big Social Data Analysis
"... People react to events, topics and entities by expressing their personal opinions and emotions. These reactions can correspond to a wide range of intensities, from very mild to strong. An adequate processing and understanding of these expressions has been the subject of research in several fields, s ..."
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People react to events, topics and entities by expressing their personal opinions and emotions. These reactions can correspond to a wide range of intensities, from very mild to strong. An adequate processing and understanding of these expressions has been the subject of research in several fields, such as business and politics. In this context, Twitter sentiment analysis, which is the task of automatically identifying and extracting subjective information from tweets, has received increasing attention from the Web mining community. Twitter provides an extremely valuable insight into human opinions, as well as new challenging Big Data problems. These problems include the processing of massive volumes of streaming data, as well as the automatic identification of human expressiveness within short text messages. In that area, several methods and lexical resources have been proposed in order to extract sentiment indicators from natural language texts at both syntactic and semantic levels. These approaches address different dimensions of opinions, such as subjectivity, polarity, intensity and emotion. This article is the first study of how these resources, which are focused on different sentiment scopes, complement each other. With this purpose we identify scenarios in which some of these resources are more useful than others. Furthermore, we propose a novel approach for sentiment classification based on meta-level features. This supervised approach boosts existing sentiment classification of subjectivity and polarity detection on Twitter. Our results show that the combination of meta-level
Que la memoria titulada EXPLOITING MULTIPLE SOURCES OF EVIDENCE FOR OPINION
, 2014
"... en el Departamento de Electrónica e Computación de la Universidade de Santiago de Compostela, y ..."
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en el Departamento de Electrónica e Computación de la Universidade de Santiago de Compostela, y
1 Sentiment Analysis A Literature Survey
, 2012
"... Our day-to-day life has always been influenced by what people think. Ideas and opinions of others have always affected our own opinions. The explosion of Web 2.0 has led to increased activity in Podcasting, Blogging, Tagging, Contributing to RSS, Social Bookmarking, and Social Networking. As a resul ..."
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Our day-to-day life has always been influenced by what people think. Ideas and opinions of others have always affected our own opinions. The explosion of Web 2.0 has led to increased activity in Podcasting, Blogging, Tagging, Contributing to RSS, Social Bookmarking, and Social Networking. As a result there has been an eruption of interest in people to mine these vast resources of data for opinions. Sentiment Analysis or Opinion Mining is the computational treatment of opinions, sentiments and subjectivity of text. In this report, we take a look at the various challenges and applications of Sentiment Analysis. We will discuss in details various approaches to perform a computational treatment of sentiments and opinions. Various supervised or data-driven techniques to SA like Naïve Byes, Maximum Entropy, SVM, and Voted Perceptrons will be discussed and their strengths and drawbacks will be touched upon. We will also see a new dimension of analyzing sentiments by Cognitive Psychology mainly through the work of Janyce Wiebe, where we will see ways to detect subjectivity, perspective in narrative and understanding the discourse structure. We will also
