Is there a language of sentiment? An analysis of lexical resources for sentiment analysis

Devitt, Ann; Ahmad, Khurshid

doi:10.1007/s10579-013-9223-6

Cited by 19 publications

(6 citation statements)

References 34 publications

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“…In this study, we analysed only those words in the categories Postiv (1915 words) and Negativ (2291 words). Constructed in the late 1960s, Devitt and Ahmad [31] explained that it is composed of frequency word lists from the Harvard IV Dictionary [32] and the Lasswell Dictionary [33]. The categories were hand-tagged essentially by ‘shrewd guesswork with numerous minor revisions’ [34].…”

Section: Sentiment Lexicons Evaluatedmentioning

confidence: 99%

Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons

Khoo

Johnkhan

2017

Journal of Information Science

188

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This article introduces a new general-purpose sentiment lexicon called WKWSCI Sentiment Lexicon and compares it with five existing lexicons: Hu & Liu Opinion Lexicon, Multi-perspective Question Answering (MPQA) Subjectivity Lexicon, General Inquirer, National Research Council Canada (NRC) Word-Sentiment Association Lexicon and Semantic Orientation Calculator (SO-CAL) lexicon. The effectiveness of the sentiment lexicons for sentiment categorisation at the document level and sentence level was evaluated using an Amazon product review data set and a news headlines data set. WKWSCI, MPQA, Hu & Liu and SO-CAL lexicons are equally good for product review sentiment categorisation, obtaining accuracy rates of 75%–77% when appropriate weights are used for different categories of sentiment words. However, when a training corpus is not available, Hu & Liu obtained the best accuracy with a simple-minded approach of counting positive and negative words for both document-level and sentence-level sentiment categorisation. The WKWSCI lexicon obtained the best accuracy of 69% on the news headlines sentiment categorisation task, and the sentiment strength values obtained a Pearson correlation of 0.57 with human-assigned sentiment values. It is recommended that the Hu & Liu lexicon be used for product review texts and the WKWSCI lexicon for non-review texts.

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Section: Sentiment Lexicons Evaluatedmentioning

confidence: 99%

Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons

Khoo

Johnkhan

2017

Journal of Information Science

188

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“…Unsupervised approaches are characterized by the use of semantic orientation (SO) dictionaries or opinion lexicons (Devitt & Ahmad, ). To classify polarity, these methods obtain the subjective expressions present in a text and aggregate their SO in a given way.…”

Section: Background and Related Workmentioning

confidence: 99%

On the usefulness of lexical and syntactic processing in polarity classification of Twitter messages

Vilares

Alonso

Gómez-Rodríguez

2015

Asso for Info Science & Tech

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Millions of micro texts are published every day on Twitter. Identifying the sentiment present in them can be helpful for measuring the frame of mind of the public, their satisfaction with respect to a product or their support of a social event. In this context, polarity classification is a subfield of sentiment analysis focussed on determining whether the content of a text is objective or subjective, and in the latter case, if it conveys a positive or a negative opinion. Most polarity detection techniques tend to take into account individual terms in the text and even some degree of linguistic knowledge, but they do not usually consider syntactic relations between words. This article explores how relating lexical, syntactic and psychometric information can be helpful to perform polarity classification on Spanish tweets. We provide an evaluation for both shallow and deep linguistic perspectives. Empirical results show an improved performance of syntactic approaches over pure lexical models when using large training sets to create a classifier, but this tendency is reversed when small training collections are used.

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“…The role of linguistic resources -mostly those concerned with lexical semantics-has been herein central: in the last decades, the success in several tasks such as word sense disambiguation has been strongly related to the development of lexical resources [51,53,57]. The same holds for specialized forms of semantic analysis and interpretation, such as sentiment analysis, where systems' efficacy [11] has been accompanied by the release of specialized lexical resources and corpora (e.g., [5,47,18]). Finally, in the last few years the creation of multilingual and parallel resources [21,58] further strengthened the link between lexical resources and successful NLP applications [16,24,56].…”

Section: Introductionmentioning

confidence: 99%

COVER: a linguistic resource combining common sense and lexicographic information

Mensa

Radicioni

Lieto

2018

Lang Resources & Evaluation

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Lexical resources are fundamental to tackle many tasks that are central to present and prospective research in Text Mining, Information Retrieval, and connected to Natural Language Processing. In this article we introduce COVER, a novel lexical resource, along with COVERAGE, the algorithm devised to build it. In order to describe concepts, COVER proposes a compact vectorial representation that combines the lexicographic precision characterizing BabelNet and the rich common-sense knowledge featuring Con-ceptNet. We propose COVER as a reliable and mature resource, that has been employed in as diverse tasks as conceptual categorization, keywords extraction, and conceptual similarity. The experimental assessment is performed on the last task: we report and discuss the obtained results, pointing out future improvements. We conclude that COVER can be directly exploited to build applications, and coupled with existing resources, as well.

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Is there a language of sentiment? An analysis of lexical resources for sentiment analysis

Cited by 19 publications

References 34 publications

Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons

Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons

On the usefulness of lexical and syntactic processing in polarity classification of Twitter messages

COVER: a linguistic resource combining common sense and lexicographic information

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