Sentiment classification of social media reviews using an ensemble classifier

Sangam, Savita; Shinde, Subhash K.

doi:10.11591/ijeecs.v16.i1.pp355-363

Cited by 13 publications

(15 citation statements)

References 20 publications

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“…Additionally, Jain et al proposed a computational model of emotion switching for an intelligent agent [24]. Sangam, Shinde combined two classifiers SVM and ANN for sentiment classification [6], it is a general model, the experiments were performed on movie reviews dataset for any language, without consideration of complex languages such as Arabic language that has been taken into consideration in our research.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Social media processing in the real world includes analysis of real problems, events, and a wide range of applications [1][2][3], as well as analysis of tweets associated with the cybersecurity problems [4,5], opinions mining, analysis of tweets associated with areas like automated business, education [6,4]or other social issues. Usually, the concentration of these analyses is on the contents given as a text segment, such as tweets, emails, messages, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning techniques are used to recognise, analyse, or classify human feelings, opinions, assessments, demeanours, and emotions toward entities such as products, administrations, people, issues, etc. [6,18]. Therefore, the ability of machines to classify users' emotions correctly should be exploited to follow up on patients' psychological states.…”

Section: Introductionmentioning

confidence: 99%

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Exploration of the best performance method of emotions classification for arabic tweets

Al-Hagery

Alassaf

Al-kharboush

2020

IJEECS

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<p><span>Arab users of social media have significantly increased, thus increasing the opportunities for extracting knowledge from various areas of life such as trade, education, psychological health services, etc. The active Arab presence on Twitter motivates many researchers to classify and analysis Arabic tweets from numerous aspects. This study aimed to explore the best performance scenarios in the classification of emotions conveyed through Arabic tweets. Hence, various experiments were conducted to investigate the effects of feature extraction techniques and the N-gram model on the performance of three supervised machine learning algorithms, which are Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR). The general method of the experiments was based on five steps; data collection, preprocessing, feature extraction, emotion classification, and evaluation of results. To implement these experiments, a real-world Twitter dataset was gathered. The best result achieved by the SVM classifier when using a bag of words (BoW) weighting schema (with unigrams and bigrams or with unigrams, bigrams, and trigrams) exceeded the best performance results of other algorithms.</span></p>

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploration of the best performance method of emotions classification for arabic tweets

Al-Hagery

Alassaf

Al-kharboush

2020

IJEECS

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“…In this section, a brief description is provided to assess the existing approaches of textual sentiment analysis and the distinction between the proposed framework and related previous works. The textual sentiment analysis is very well-known as part of data mining approaches, which can essentially classify the sentimental multi-view textual data into two categories as positive or negative [11]. In this research, the proposed framework is applied on online polarity user-reviews to be classified as 'good' or 'bad' sentiments.…”

Section: Related Workmentioning

confidence: 99%

Sentimental classification analysis of polarity multi-view textual data using data mining techniques

Yassir

Mohammed

Alkhazraji

et al. 2020

IJECE

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The data and information available in most community environments is complex in nature. Sentimental data resources may possibly consist of textual data collected from multiple information sources with different representations and usually handled by different analytical models. These types of data resource characteristics can form multi-view polarity textual data. However, knowledge creation from this type of sentimental textual data requires considerable analytical efforts and capabilities. In particular, data mining practices can provide exceptional results in handling textual data formats. Besides, in the case of the textual data exists as multi-view or unstructured data formats, the hybrid and integrated analysis efforts of text data mining algorithms are vital to get helpful results. The objective of this research is to enhance the knowledge discovery from sentimental multi-view textual data which can be considered as unstructured data format to classify the polarity information documents in the form of two different categories or types of useful information. A proposed framework with integrated data mining algorithms has been discussed in this paper, which is achieved through the application of X-means algorithm for clustering and HotSpot algorithm of association rules. The analysis results have shown improved accuracies of classifying the sentimental multi-view textual data into two categories through the application of the proposed framework on online polarity user-reviews dataset upon a given topics.

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“…For example, opinion messages are used to track consumers' attitudes toward products or services. Moreover, they are used to identify the satisfactions of demographics features with particular products [1,2]. In politics, opinion messages are used for electoral predictions or to make a survey of people's opinions about political parties [3].…”

Section: Introductionmentioning

confidence: 99%

Opinion classification on a social network by a novel feature selection technique

Choompol

Songram

Chomphuwiset

2020

IJEECS

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Most of the opinion comments on social networks are short and ambiguous. In general, opinion classification on the comments is difficult because of lacking dominant features. A feature extraction technique is therefore necessary for improving accuracy of the classification and computational time. This paper proposes an effective feature selection method for opinion classification on a social network. The proposed method selects features based on the concept of a filter model, together with association rules. Support and confidence are used to calculate the weights of features. The features with high weight are selected for classification. Unlike supports in association rules, supports in our method are normalized to 0-1 to remove outlier supports. Moreover, a tuning parameter is used to emphasize the degree of support or confidence. The experimental results show that the proposed method provides high classification efficiency. The proposed method outperforms Information Gain, Chi-Square, and Gini Index in both computational time and accuracy.

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Sentiment classification of social media reviews using an ensemble classifier

Cited by 13 publications

References 20 publications

Exploration of the best performance method of emotions classification for arabic tweets

Exploration of the best performance method of emotions classification for arabic tweets

Sentimental classification analysis of polarity multi-view textual data using data mining techniques

Opinion classification on a social network by a novel feature selection technique

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