Multiclass Sentiment Analysis of Social Media Data using Neural Networks
Nirmal Varghese Babu1, Fabeela Ali Rawther2
1Nirmal Varghese Babu, Department of Computer Science and Engineering, Amal Jyothi College of Engineering, Kanjirappaly (Kerala), India.
2Fabeela Ali Rawther, Department of Computer Science and Engineering, Amal Jyothi College of Engineering, Kanjirappaly (Kerala), India.
Manuscript received on 14 December 2019 | Revised Manuscript received on 22 December 2019 | Manuscript Published on 31 December 2019 | PP: 57-62 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10121291S319/19©BEIESP | DOI: 10.35940/ijeat.A1012.1291S319
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Abstract: Sentiment analysis, also known as Opinion Mining is one of the hottest topic Nowadays. in various social networking sites is one of the hottest topic and field nowadays. Here, we are using Twitter, the biggest web destinations for people to communicate with each other to perform the sentiment analysis and opinion mining by extracting the tweets by various users. The users can post brief text updates in twitter as it only allows 140 characters in one text message. Hashtags helps to search for tweets dealing with the specified subject. In previous researches, binary classification usually relies on the sentiment polarity(Positive , Negative and Neutral). The advantage is that multiple meaning of the same world might have different polarity, so it can be easily identified. In Multiclass classification, many tweets of one class are classified as if they belong to the others. The Neutral class presented the lowest precision in all the researches happened in this particular area. The set of tweets containing text and emoticon data will be classified into 13 classes. From each tweet, we extract different set of features using one hot encoding algorithm and use machine learning algorithms to perform classification. The entire tweets will be divided into training data sets and testing data sets. Training dataset will be pre-processed and classified using various Artificial Neural Network algorithms such as Reccurent Neural Network, Convolutional Neural Network etc. Moreover, the same procedure will be followed for the Text and Emoticon data. The developed model or system will be tested using the testing dataset. More precise and correct accuracy can be obtained or experienced using this multiclass classification of text and emoticons. 4 Key performance indicators will be used to evaluate the effectiveness of the corresponding approach.
Keywords: Multiclass Sentiment Analysis, Data Pre-processing, Natural Language Processing, Feature Extraction, Classification, Emoticons, Neural Networks.
Scope of the Article: Social Networks