Twitter Sentiment Analysis for Large-Scale Data: An Unsupervised Approach

Pandarachalil, Rafeeque; Sendhilkumar, S.; Mahalakshmi, G. S.

doi:10.1007/s12559-014-9310-z

Cited by 114 publications

(39 citation statements)

References 18 publications

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“…In general, researchers have focused on the analytics and utilization, having paid little attention to clarifying the very concept of BSD and understanding the related phenomena (for example, [19][20][21]). …”

Section: Central Concepts and Goals Of The Researchmentioning

confidence: 99%

Conceptualizing Big Social Data

et al. 2017

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Section: Central Concepts and Goals Of The Researchmentioning

confidence: 99%

Conceptualizing Big Social Data

et al. 2017

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“…As given by Pandarachalil et al [12], machine learning approaches require a huge amount of labeled training data to achieve desired accuracy. In twitter domain, this is not always practical.…”

Section: Classification Phasementioning

confidence: 99%

“…A detailed survey about new methodologies for social big data, social data analysis, providing also social-based applications, are presented by Orgaz et al [11]. Pandarachalil et al [12] presented an unsupervised method for SA of Twitter data. To predict American presidential elections, Shei et al [13] analyzed location-based Twitter data with SA in the first stage and proposed a feature model in the second stage.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis: an Application to Anadolu University

2017

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Social media is a Web 2.0 platform that allows to share content and information without the limitations of time and space. Social media networks have managed to become a part of today's lifestyle and are increasingly gaining importance when viewed from a state perspective. Sentiment analysis refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials. In this study, we focus on social media mining and sentiment analysis for students of an open and distance education system. Anadolu University which has approximately two million students and more than two million graduates, is a well-known institution in Turkey, that offers higher education through contemporary distance education model. Firstly, we have fetched Tweets related to Anadolu University open and distance education system. To perform sentiment analysis, these tweets were analysed by statistical and data mining techniques. Finally, results were visualized.

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“…A more recent work in [18] considers analogous topics. Work in [19] proposes an unsupervised approach that involves the extraction of terms and slangs polarities from three sentiment lexicons and the aggregation of such scores to predict the overall sentiment of a tweet. In [4,5,20], the authors consider the contextual polarity of a word, i.e., the polarity acquired by the word contextually to the sentence in which it appears.…”

Section: Related Workmentioning

confidence: 99%

A Study on Text-Score Disagreement in Online Reviews

et al. 2017

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Background/Introduction. In this paper, we focus on online reviews and employ artificial intelligence tools, taken from the cognitive computing field, to help understanding the relationships between the textual part of the review and the assigned numerical score. We move from the intuitions that 1) a set of textual reviews expressing different sentiments may feature the same score (and vice-versa); and 2) detecting and analyzing the mismatches between the review content and the actual score may benefit both service providers and consumers, by highlighting specific factors of satisfaction (and dissatisfaction) in texts.Methods. To prove the intuitions, we adopt sentiment analysis techniques and we concentrate on hotel reviews, to find polarity mismatches therein. In particular, we first train a text classifier with a set of annotated hotel reviews, taken from the Booking website. Then, we analyze a large dataset, with around 160k hotel reviews collected from Tripadvisor, with the aim of detecting a polarity mismatch, indicating if the textual content of the review is in line, or not, with the associated score.Results. Using well established artificial intelligence techniques and analyzing in depth the reviews featuring a mismatch between the text polarity and the score, we find that -on a scale of five stars -those reviews ranked with middle scores include a mixture of positive and negative aspects.Conclusions. The approach proposed here, beside acting as a polarity detector, provides an effective selection of reviews -on an initial very large dataset -that may allow both consumers and providers to focus directly on the review subset featuring a text/score disagreement, which conveniently convey to the user a summary of positive and negative features of the review target.

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Twitter Sentiment Analysis for Large-Scale Data: An Unsupervised Approach

Cited by 114 publications

References 18 publications

Conceptualizing Big Social Data

Conceptualizing Big Social Data

Sentiment Analysis: an Application to Anadolu University

A Study on Text-Score Disagreement in Online Reviews

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