A survey on classification techniques for opinion mining and sentiment analysis

Hemmatian, Fatemeh; Sohrabi, Mohammad Karim

doi:10.1007/s10462-017-9599-6

Cited by 292 publications

(162 citation statements)

References 188 publications

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“…Surveys that include discussions on the existing work and that summarize the open challenges and issues of opinion mining and sentiment analysis are presented in Patil and Atique (2015), Hussein (2018), Hemmatian and Sohrabi (2019), Zhang, Wang, and Liu (2018).…”

Section: Salasmentioning

confidence: 99%

“…Opinion mining and sentiment analysis are considered as subfields of natural language processing, information retrieval, and text mining. Opinion mining is the process of extracting users' opinions and thoughts expressed on entities or features/ aspects of entities from unstructured texts, while sentiment analysis is the process of analyzing the opinionated text and determining its polarity in an automated manner (Hemmatian & Sohrabi, 2019). Sentiment analysis can be carried out at various structural levels in text ranging from individual words to entire documents.…”

Section: Introductionmentioning

confidence: 99%

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An enhanced feature‐based sentiment analysis approach

Saeed

Helal

Badr

et al. 2019

WIREs Data Min & Knowl

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In the last few years, online reviews where individuals express their thoughts, interests, experiences, and opinions have broadly spread over the internet. Sentiment analysis has evolved to analyze these online reviews and provide valuable insights for both individuals and organizations that may help them in making decisions. Unfortunately the performance of sentiment analysis process is affected by the nature of online reviews' content that may contain emoticons and negation words. Moreover, spam reviews have been written for the purpose of deceiving others. Therefore, there is a need to develop an approach that considers these issues. In this paper, an enhanced approach for sentiment analysis is proposed which aims to enhance the performance of classifying reviews based on their features and assigning accurate sentiment score to features. This enhanced approach is achieved by handling negation, detecting emoticons, and detecting spam reviews using a combination of different types of properties which leads to achieving better predictive performance. The proposed approach has been verified against three datasets of different sizes. The results indicate that the proposed approach achieves a maximum accuracy of about 99.06% in detecting spam reviews and a maximum accuracy of about 97.13% in classifying reviews. This article is categorized under: Algorithmic Development > Text Mining Technologies > Classification Technologies > Machine Learning

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Section: Salasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An enhanced feature‐based sentiment analysis approach

Saeed

Helal

Badr

et al. 2019

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

“…It is derived from WordNet. Each synset (noun, verb, adjective, and adverb) in SentiWordNet is assigned three types of sentiment scores (polarity) namely, positivity Pos(S) , negativity Neg(S) , and objectivity Obj(S) , ranged in the [0, 1] interval and in sum equal to 1.0 . WordNet is a “dictionary of meaning” integrating the functions of dictionaries and thesauruses.…”

Section: Background Knowledge and Related Workmentioning

confidence: 99%

Educational guidance process integrating opinion mining

Lazib

Bouarab-Dahmani

Haddadi

2019

Comp Applic In Engineering

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Opinion mining is a fast-developing field of study with numerous applications.It raises economic and political interests, especially in decision-making processes. In this paper, we are interested in educational opinion mining. We propose an educational guidance process that aims to improve university orientation after the secondary school cycle. Unlike the ordinary orientation processes, our process integrates the opinions of pupil, parents, and teachers, collected periodically. These opinions are treated to sort out the studies' domains in a positive polarity order. An orientation score is then calculated based on these opinions, the student's aptitudes and the continuous evaluation marks to guide pupils toward the most favourable discipline. The experimentations conducted on a sample of pupils (300) give a better convergence to the pupils' explicit choices using our proposal with a κ coefficient of 0.71, against 0.29 using only marks. K E Y W O R D Seducational guidance, opinion mining, opinion mining for education, opinion synthesis, pupil's opinions

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“…Sentiment analysis is a popular data mining and NLP topic both in the industry and academia . The task of sentiment analysis can be a binary classification of positive and negative opinions, as well as extracting the strength of the sentiment…”

Section: Deep Learning Models For Sentiment Classificationmentioning

confidence: 99%

“…Sentiment analysis is a popular data mining and NLP topic both in the industry and academia. 33,34 The task of sentiment analysis can be a binary classification of positive and negative opinions, 35,36 as well as extracting the strength of the sentiment. 37,38 Wehrmann et al 39 proposed a language agnostic sentiment analysis model that can be built on text from different languages.…”

Section: Sentiment Analysismentioning

confidence: 99%

Evaluating deep learning models for sentiment classification

Karakus

Talo

Hallac

et al. 2018

Concurrency and Computation

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Summary Deep learning has emerged as an effective solution to various text mining problems such as document classification and clustering, document summarization, web mining, and sentiment analysis. In this paper, we describe our work on investigating several deep learning models for a binary sentiment classification problem. We used movie reviews in Turkish from the website http://www.beyazperde.com to train and test the deep learning models. We also report a detailed comparison of the models in terms of accuracy and time performances. Two major deep learning architectures used in this study are Convolutional Neural Networks and Long Short‐Term Memory. We built several variants of these models by changing the number of layers, tuning the hyper‐parameters, and combining models. Additionally, word embeddings were created by applying the word2vec algorithm with a skip‐gram model on a large dataset (∼ 13 M words) composed of movie reviews. We investigate the effect of using the pre‐word embeddings with these models. Experimental results have shown that the use of word embeddings with deep neural networks effectively yields performance improvements in terms of run time and accuracy.

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A survey on classification techniques for opinion mining and sentiment analysis

Cited by 292 publications

References 188 publications

An enhanced feature‐based sentiment analysis approach

An enhanced feature‐based sentiment analysis approach

Educational guidance process integrating opinion mining

Evaluating deep learning models for sentiment classification

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