Sentiment/subjectivity analysis survey for languages other than English

Korayem, Mohammed; AlJadda, Khalifeh; Crandall, David J.

doi:10.1007/s13278-016-0381-6

Cited by 23 publications

(12 citation statements)

References 28 publications

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“…Therefore, the most efficient method to be employed when building sentiment analysis systems is the employment of tools with language-specific features. Although building resources for each language is costly, it is the most accurate way to be followed [23].…”

Section: Methodsmentioning

confidence: 99%

A real-time social network-based knowledge discovery system for decision making

Yüksel

Tan

2018

Automatika

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The increasing amount of data in social networks has complicated data processing and interpretation. Therefore, intelligent decision-support mechanisms that have the ability to automatically extract meaning from data and interpret the opinions of people in real time have become inevitable. In this study, an intelligent multilingual decision support system was implemented, and a new algorithm that employs text mining and sentiment analysis techniques was developed to automatically interpret the opinions of social network users about the places they plan to visit. The system can be used as a baseline for sentiment analysis in social networks and can be adapted to build new systems. In this study, we set our main focus on Turkish language and show the applicability of our approach for other languages through the experiments for English language. The dataset required for the implementation of text mining techniques was created based on the venue recommendations shared on Foursquare social media platform. As a result, a contribution was made to the way the social network users make decisions without reading thousands of recommendations. Our results show that the developed system achieves classification accuracy of 84.49% for Turkish and 95% for English. Finally, the most liked or disliked foods/beverages are correctly identified for 107 out of 128 venues.

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Section: Methodsmentioning

confidence: 99%

A real-time social network-based knowledge discovery system for decision making

Yüksel

Tan

2018

Automatika

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“…It may be tacked as a classification problem, where the goal is to determine whether a written document, e.g. reviews, expresses a positive or negative opinion about specific entities [5,24]. Works in sentiment analysis domain can be classified into statistical approaches also known as lexical based and supervised learning or corpus-based approaches [7].…”

Section: Literature Reviewmentioning

confidence: 99%

An Enhanced Corpus for Arabic Newspapers Comments

Rahab

Zitouni

Djoudi

2020

IAJIT

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In this paper, we propose our enhanced approach to create a dedicated corpus for Algerian Arabic newspapers comments. The developed approach has to enhance an existing approach by the enrichment of the available corpus and the inclusion of the annotation step by following the Model Annotate Train Test Evaluate Revise (MATTER) approach. A corpus is created by collecting comments from web sites of three well know Algerian newspapers. Three classifiers, support vector machines, naïve Bayes, and k-nearest neighbors, were used for classification of comments into positive and negative classes. To identify the influence of the stemming in the obtained results, the classification was tested with and without stemming. Obtained results show that stemming does not enhance considerably the classification due to the nature of Algerian comments tied to Algerian Arabic Dialect. The promising results constitute a motivation for us to improve our approach especially in dealing with non Arabic sentences, especially Dialectal and French ones

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“…Zhang et al [16] was pioneer of the sentiment analysis in Chinese language. This system used the methodology of rulebased system.…”

Section: Chinesementioning

confidence: 99%