Automatic Approach of Sentiment Lexicon Generation for Mobile Shopping Reviews

Feng, Jun; Gong, Cheng; Li, Xiaodong; Lau, Raymond Y. K.

doi:10.1155/2018/9839432

Cited by 18 publications

(16 citation statements)

References 32 publications

(32 reference statements)

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“…Hybrid approaches based on lexicon generation for specific languages have been useful in sentiment detection which could help improve the task of cyberbullying detection. These hybrid approaches may be classified into corpora-based and dictionary based (AlHarbi et al , 2019; Feng et al , 2018). Similarly, several analysis techniques have been applied to tweets for analysis of sentiment to detect the perception of travelers toward carpooling (Ciasullo et al , 2018).…”

Section: Related Workmentioning

confidence: 99%

Cyberbullying detection from tweets using deep learning

Bharti

Yadav

Kumar

et al. 2021

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PurposeWith the rise of social media platforms, an increasing number of cases of cyberbullying has reemerged. Every day, large number of people, especially teenagers, become the victim of cyber abuse. A cyberbullied person can have a long-lasting impact on his mind. Due to it, the victim may develop social anxiety, engage in self-harm, go into depression or in the extreme cases, it may lead to suicide. This paper aims to evaluate various techniques to automatically detect cyberbullying from tweets by using machine learning and deep learning approaches.Design/methodology/approachThe authors applied machine learning algorithms approach and after analyzing the experimental results, the authors postulated that deep learning algorithms perform better for the task. Word-embedding techniques were used for word representation for our model training. Pre-trained embedding GloVe was used to generate word embedding. Different versions of GloVe were used and their performance was compared. Bi-directional long short-term memory (BLSTM) was used for classification.FindingsThe dataset contains 35,787 labeled tweets. The GloVe840 word embedding technique along with BLSTM provided the best results on the dataset with an accuracy, precision and F1 measure of 92.60%, 96.60% and 94.20%, respectively.Research limitations/implicationsIf a word is not present in pre-trained embedding (GloVe), it may be given a random vector representation that may not correspond to the actual meaning of the word. It means that if a word is out of vocabulary (OOV) then it may not be represented suitably which can affect the detection of cyberbullying tweets. The problem may be rectified through the use of character level embedding of words.Practical implicationsThe findings of the work may inspire entrepreneurs to leverage the proposed approach to build deployable systems to detect cyberbullying in different contexts such as workplace, school, etc and may also draw the attention of lawmakers and policymakers to create systemic tools to tackle the ills of cyberbullying.Social implicationsCyberbullying, if effectively detected may save the victims from various psychological problems which, in turn, may lead society to a healthier and more productive life.Originality/valueThe proposed method produced results that outperform the state-of-the-art approaches in detecting cyberbullying from tweets. It uses a large dataset, created by intelligently merging two publicly available datasets. Further, a comprehensive evaluation of the proposed methodology has been presented.

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Section: Related Workmentioning

confidence: 99%

Cyberbullying detection from tweets using deep learning

Bharti

Yadav

Kumar

et al. 2021

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“…Feng et al (2018) recommended an automatic SL generation approach for reviews on mobile shopping. Primarily, the method picked the product features and sentiment words as of the original reviews and then mined their relation centered on an enhanced PMI algorithm.…”

Section: Related Workmentioning

confidence: 99%

PMI-based polarity computation for SVM-NN-based sentiment classification from user-generated reviews

Padmavathy

Mohideen

Gulzar

2021

IJIUS

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PurposeThe purpose of this paper is to initially perform Senti-WordNet (SWN)- and point wise mutual information (PMI)-based polarity computation and based polarity updation. When the SWN polarity and polarity mismatched, the vote flipping algorithm (VFA) is employed.Design/methodology/approachRecently, in domains like social media(SM), healthcare, hotel, car, product data, etc., research on sentiment analysis (SA) has massively increased. In addition, there is no approach for analyzing the positive or negative orientations of every single aspect in a document (a tweet, a review, as well as a piece of news, among others). For SA as well as polarity classification, several researchers have used SWN as a lexical resource. Nevertheless, these lexicons show lower-level performance for sentiment classification (SC) than domain-specific lexicons (DSL). Likewise, in some scenarios, the same term is utilized differently between domain and general knowledge lexicons. While concerning different domains, most words have one sentiment class in SWN, and in the annotated data set, their occurrence signifies a strong inclination with the other sentiment class. Hence, this paper chiefly concentrates on the drawbacks of adapting domain-dependent sentiment lexicon (DDSL) from a collection of labeled user reviews and domain-independent lexicon (DIL) for proposing a framework centered on the information theory that could predict the correct polarity of the words (positive, neutral and negative). The proposed work initially performs SWN- and PMI-based polarity computation and based polarity updation. When the SWN polarity and polarity mismatched, the vote flipping algorithm (VFA) is employed. Finally, the predicted polarity is inputted to the mtf-idf-based SVM-NN classifier for the SC of reviews. The outcomes are examined and contrasted to the other existing techniques to verify that the proposed work has predicted the class of the reviews more effectually for different datasets.FindingsThere is no approach for analyzing the positive or negative orientations of every single aspect in a document (a tweet, a review, as well as a piece of news, among others). For SA as well as polarity classification, several researchers have used SWN as a lexical resource. Nevertheless, these lexicons show lower-level performance for sentiment classification (SC) than domain-specific lexicons (DSL). Likewise, in some scenarios, the same term is utilized differently between domain and general knowledge lexicons. While concerning different domains, most words have one sentiment class in SWN, and in the annotated data set their occurrence signifies a strong inclination with the other sentiment class.Originality/valueThe proposed work initially performs SWN- and PMI-based polarity computation, and based polarity updation. When the SWN polarity and polarity mismatched, the vote flipping algorithm (VFA) is employed.

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“…A considerable amount of literature has concentrated on analyzing documents' sentiment polarity [9]. Automatic sentiment classification has been extensively applied to reviews of products [10], movies [11], books [12], shopping [13], social networks [14], and students' course evaluation comments [15,16], which is a common application of classifying positive or negative reviews. Most recent work has involved in extracting the textual information in the financial reports, as the text may contain more information than the numerical part in an annual report [17,18].…”

Section: Sentiment Analysismentioning

confidence: 99%

Anticipating Corporate Financial Performance from CEO Letters Utilizing Sentiment Analysis

Che

Zhu

2020

Mathematical Problems in Engineering

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With the emergence and tremendous growth of text mining, a computer-assisted approach for capturing sentiment viewpoints from textual data is gradually becoming a promising field, particularly when researchers are increasingly facing the problem of filtering bunches of useless information without capturing the essence in the big data era. This study aims at observing and classifying the sentiment orientation in CEO letters, digging the main corporate social responsibility (CSR) themes, and examining the effectiveness of CEO letters’ sentiment on forecasting financial performance. A specific sentiment dictionary has been proposed to identify and classify the sentiment orientation in CEO letters by utilizing the appraisal theory. Additionally, the qualitative data analysis software NVivo is applied to explore the CSR topics. Furthermore, a modified Altman’s Z-score model and machine-learning approach are employed to predict financial performance. The results of preliminary evaluations validate that approximately 62.14% of the texts represent positive polarity even when companies are not in a promising economic situation. The CSR themes mainly focus on business ethical responsibility, particularly ethical activities. Among various machine-learning approaches, the logistic regression approach is appropriate for predicting financial performance with the state-of-the-art accuracy of 70.46 %. The encouraging results indicate that the sentiment information inCEO letters is a vital factor for anticipating financial performance. This work not only offers a new analytic framework for associating linguistic theory with computer science and economic models but will also improve stakeholders’ decision-making.

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Automatic Approach of Sentiment Lexicon Generation for Mobile Shopping Reviews

Cited by 18 publications

References 32 publications

Cyberbullying detection from tweets using deep learning

Cyberbullying detection from tweets using deep learning

PMI-based polarity computation for SVM-NN-based sentiment classification from user-generated reviews

Anticipating Corporate Financial Performance from CEO Letters Utilizing Sentiment Analysis

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