Apply word vectors for sentiment analysis of APP reviews

Fan, Xin; Li, Xiaoge; Du, Feihong; Li, Xin; Wei, Mian

doi:10.1109/icsai.2016.7811108

Cited by 35 publications

(8 citation statements)

References 2 publications

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“…Significant amount of work has been done on sentiment analysis in past few years, but we have focused on two techniques machine learning and lexicon based that are widely used for different Indian 1 [10]- [21]. For the NLP task, they have utilized vector portrayals for effective use of word vector portrayal which gives feeling examination issue arrangement [8]. They thought about part of speech and vocabulary functionality alongside the artificial intelligence (AI) approach in particular support vector machine, logistic regression, and naïve Bayes [22].…”

Section: Literature Surveymentioning

confidence: 99%

“…Next advance is feature determination [6], [7]. Numerous strategies are accessible for feature determination, for example, a bunch of words, TF-IDF, count vectors, and word embeddings which depend on characteristic language preparing [8], [9]. The last advance is to apply AI calculation for the order of perspective, for example, K-nearest-neighbors (KNN) and multinomial naive Bayes (MNB).…”

Section: Introductionmentioning

confidence: 99%

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Sentiment analysis on film review in Gujarati language using machine learning

Shah

Swaminarayan

Patel

2022

IJECE

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<span>Opinion analysis is by a long shot most basic zone of characteristic language handling. It manages the portrayal of information to choose the motivation behind the wellspring of the content. The reason might be of a type of gratefulness (positive) or study (negative). This paper offers a correlation between the outcomes accomplished by applying the calculation arrangement using various classifiers for instance K-nearest neighbor and multinomial naive Bayes. These techniques are utilized to assess a significant assessment with either a positive remark or negative remark. The gathered information considered on the grounds of the extremity film datasets and an association with the results accessible proof has been created for a careful assessment. This paper investigates the word level count vectorizer and term frequency inverse document frequency (TF-IDF) influence on film sentiment analysis. We concluded that multinomial Naive Bayes (MNB) classier generate more accurate result using TF-IDF vectorizer compared to CountVectorizer, K-nearest-neighbors (KNN) classifier has the same accuracy result in case of TF-IDF and CountVectorizer.</span>

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Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sentiment analysis on film review in Gujarati language using machine learning

Shah

Swaminarayan

Patel

2022

IJECE

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“…On the basis of correlated on even predictive values of DJIA the large-scale collection of tweets is measured. In the paper published by Xian Fan et al [20] it was mentioned that on application of word vectors for analysis of sentiment of APP, the specification of reviews was done as per their investigation on it which specifies the effectiveness of the representations as vectors over different kinds of text data and the quality of domain-dependent vector was evaluated. They have concluded that the huge amount of meaningful researches will be done using the sentiment which are produced from the aspects.…”

Section: Related Workmentioning

confidence: 99%

An Integrated Approach to Sentiment Analysis using Machine Learning Algorithms

Totakura¹

2020

IJRASET

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Sentiment Analysis (SA) is nothing but mining the emotion from many sources. Some of them include texts, audio, video, etc. Every individual has their own opinion, hence own reviews and ratings. Based on these reviews if we classify the sentiment of the opinion of the public, the profit or loss calculations on the product or application is directly found. Our algorithm takes Naïve Bayes (NB) as a foundation of classification of textual data taken from the public and categorizes the tweets accordingly. To this, we are adding an organic emotion factor called Average Impact Factor (AMF). In the market, there were several algorithms which can be used in mining the sentiment from the given textual data. But this sentiment has flaws as it cannot detect the true emotion from the text or it overfits the opinion of the public. Based on this idea, we integrated the AMF on the public tweets and reviews to evaluate the true sentiment and to improve the time factor too of the opinion mining. We used data of tweets related to Demonetization that happened in India, 2016. When compared to NB Classifier and Support Vector Machine (SVM) algorithms, there is an improvement in time constraint and accuracy too in our Integrated Sentimental Analysis (ISA) classifier.

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“…In [24], Giatsoglou et al observed that LDA is computationally very expensive as compared to LSA on large datasets. In contrast, Fan et al used Naive Bayes as the classification method to build sentiment lexicons through word vectors matrices separately and then used the Boolean rules to classify the matched documents for polarity that appeared in both matrices [38]. An extended model for sentiment classification [16], is presented by Haocheng et al in [42], which focused on the semantic features between words rather than the simple lexical or syntactic features.…”

Section: Related Workmentioning

confidence: 99%

Senti2vec: An Effective Feature Extraction Technique for Sentiment Analysis Based on Word2vec

Alshari

Azman

Doraisamy

et al. 2020

MJCS

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The discovery of an active feature extraction technique has been the focus of many researchers to improve the performance of classification methods, such as for sentiment analysis. Many of them have shown interest in using word embeddings especially Word2Vec as the features for text classification tasks. Its ability to model high-quality distributional semantics among words has contributed to its success in many of the functions. Despite the success, Word2Vec features are high dimensional that lead to an increase in the complexity of the classifier. In this paper, an effective method for feature extraction based on Word2Vec is proposed for sentiment analysis. The process discovers polarity clusters of the terms in the vocabulary through Word2Vec and opinion lexical dictionary. The features vector for each text is constructed from the polarity clusters, which lead to a lower-dimensional vector to represent the text. This paper also investigates the effect of two opinion lexical dictionaries on the performance of sentiment analysis, and one of the dictionaries are created based on SentiWordNet. The effectiveness of the proposed method is evaluated on the IMDB with two classifiers, namely the Logistic Regression and the Support Vector Machine. The result is promising, showing that the proposed method can be more effective than the baseline approaches.

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Apply word vectors for sentiment analysis of APP reviews

Cited by 35 publications

References 2 publications

Sentiment analysis on film review in Gujarati language using machine learning

Sentiment analysis on film review in Gujarati language using machine learning

An Integrated Approach to Sentiment Analysis using Machine Learning Algorithms

Senti2vec: An Effective Feature Extraction Technique for Sentiment Analysis Based on Word2vec

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