Sentence Level Sentiment Analysis in the Presence of Conjuncts Using Linguistic Analysis

Meena, Arun; Prabhakar, T. V.

doi:10.1007/978-3-540-71496-5_53

Cited by 87 publications

(51 citation statements)

References 7 publications

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“…In addition, Wilson et al [32] raise the question of obtaining clause-level opinion strength as a preparation step for sentence-level sentiment analysis. In [33,34] researchers focused on sentence polarities separately, again to obtain sentence polarities more correctly, with the goal of improving review polarity in turn. The first line polarity has also been used as a feature by [8].…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis Using Domain-Adaptation and Sentence-Based Analysis

Gezi̇ci

Yanıkoğlu

Tapucu

et al. 2015

Advances in Social Media Analysis

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Sentiment analysis aims to automatically estimate the sentiment in a given text as positive, objective or negative, possibly together with the strength of the sentiment. Polarity lexicons that indicate how positive or negative each term is, are often used as the basis of many sentiment analysis approaches. Domain-specific polarity lexicons are expensive and time-consuming to build; hence, researchers often use a general purpose or domain-independent lexicon as the basis of their analysis. In this work, we address two sub-tasks in sentiment analysis. We apply a simple method to adapt a general purpose polarity lexicon to a specific domain [1]. Subsequently, we propose and evaluate new features to be used in a word polarity based approach to sentiment classification. In particular, we analyze sentences as the first step for estimating the overall review polarity. We consider different aspects of sentences, such as length, purity, irrealis content, subjectivity, and position within the opinionated text. This analysis is then used to find sentences that may convey better information about the overall review polarity. We use a subset of hotel reviews from the TripAdvisor database [2] to evaluate the effect of sentence-level features on sentiment classification. Then, we measure the performance of our sentiment analysis engine using the domain-adapted lexicon on a large subset of the TripAdvisor database.

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Section: Related Workmentioning

confidence: 99%

Sentiment Analysis Using Domain-Adaptation and Sentence-Based Analysis

Gezi̇ci

Yanıkoğlu

Tapucu

et al. 2015

Advances in Social Media Analysis

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“…In addition, the SD scheme is especially popular in parser evaluation works (Cer et al 2010;Nivre et al 2010;Clegg and Shepherd 2007;Miwa et al 2010;Foster et al 2011), and several parsers are capable of producing the scheme either natively or by conversion, including the Charniak-Johnson parser (Charniak and Johnson 2005), the Stanford parser (Klein and Manning 2003), the Clear parser Choi and Palmer (2011), the parser of Tratz and Hovy (2011), and naturally any dependency parser that can be trained from a treebank, such as the MaltParser (Nivre et al 2007), the MSTParser (McDonald et al 2006) or the MateTools parser (Bohnet 2010). The scheme was originally intended to be applicationoriented, and it has indeed been successfully used in applications, particularly in the biomedical domain (Björne et al 2010;Miyao et al 2009;Qian and Zhou 2012), and otherwise in opinion extraction (Zhuang et al 2006) and sentiment analysis (Meena and Prabhakar 2007).…”

Section: Dependency Annotation Schemementioning

confidence: 99%

Building the essential resources for Finnish: the Turku Dependency Treebank

Haverinen

Nyblom

Viljanen

et al. 2013

Lang Resources & Evaluation

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In this paper, we present the final version of a publicly available treebank of Finnish, the Turku Dependency Treebank. The treebank contains 204,399 tokens (15,126 sentences) from 10 different text sources and has been manually annotated in a Finnishspecific version of the well-known Stanford Dependency scheme. The morphological analyses of the treebank have been assigned using a novel machine learning method to disambiguate readings given by an existing tool. As the second main contribution, we present the first open source Finnish dependency parser, trained on the newly introduced treebank. The parser achieves a labeled attachment score of 81 %. The treebank data as well as the parsing pipeline are available under an open license at

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“…No matter what kind of topic representation (probabilistic topic model, a set of keywords, or other form) is used, we should guarantee that the contents of different topics in documents can be correctly separated. Also, many researchers [4,7,12,14] agree that the semantics of expression is critical in performing sentiment analysis. Take the sentence "He is cute, still I do not like him."…”

Section: Fig 1 a Web Blog Text Snippetmentioning

confidence: 99%

“…Some (e.g., [5,8,19]) study the semantic orientation (positive or negative) of English words, especially adjectives. Some (e.g., [12]) focus on sentence level sentiment analysis; they use language grammar based rules and adopt words' semantic orientation information to obtain the sentiment polarity of a sentence. A majority of the remaining works focus on document level sentiment analysis; some (e.g., [3,4,15,18]) use machine learning classifiers to determine the overall sentiment of a document, some [10] performs feature level sentiment analysis and offer sentiment summary of product reviews as commercial products usually contain several important features based on which users can make choice.…”

Section: Related Workmentioning

confidence: 99%

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Topic Sentiment Change Analysis

Jiang

Meng

2011

Machine Learning and Data Mining in Pattern Recognition

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Abstract. Public opinions on a topic may change over time. Topic Sentiment change analysis is a new research problem consisting of two main components: (a) mining opinions on a certain topic, and (b) detect significant changes of sentiment of the opinions on the topic and identify possible reasons causing each such change. In this paper, we discuss topic sentiment change analysis using data on the Web. We adopt probabilistic topic model and language grammar based sentiment analysis techniques, and integrate them together into a topic level sentiment analysis method. This method is capable of analyzing sentiment and identifying sentiment changes of a given topic from a set of documents covering this topic and possibly other topics. In addition, as the contents of relevant topics are differentiated, our method is also able to identify hot events which are possible causes of a sentiment change. Experimental results show that our method is very promising.

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Sentence Level Sentiment Analysis in the Presence of Conjuncts Using Linguistic Analysis

Cited by 87 publications

References 7 publications

Sentiment Analysis Using Domain-Adaptation and Sentence-Based Analysis

Sentiment Analysis Using Domain-Adaptation and Sentence-Based Analysis

Building the essential resources for Finnish: the Turku Dependency Treebank

Topic Sentiment Change Analysis

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