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Sentiment analysis is a very popular technique for social network analysis. Sentiment analysis also termed as opinion mining is a process of automatically extracting knowledge from sentiments or opinions of others about some topic or problem. We can identify opinions in a large unstructured/structured data and analyze the polarity of opinions. Twitter is a large and rapidly growing micro blogging social networking website where people express their opinions in a short and simple manner of expressions. It is a common practice that merchants selling products on the Web ask their customers to review the products. In twitter number of customer reviews on different products is appearing. Mobile phones are a common domain in which number of customer reviews appears. This makes it difficult for a potential customer to read them in order to make a decision on whether to buy the product. We are only interested in the specific features of the phones that customers have opinions on and also whether the opinions are positive or negative. This paper presents a lexicon based approach for analyzing the customer reviews on mobile phones over Twitter data to measure the popularity based on which the customer can decide whether to buy the product.

Analyzing and gathering the people’s reactions on product trading, public services, etc. are crucial. Sentiment analysis (also termed as opinion mining) is a usual dialogue preparing act that plans on discovering the sentiments after opinions in texts on changing subjects. This research work adopts a novel sentiment analysis approach that comprises six phases like (i) Pre-processing, (ii) Keyword extraction and its sentiment categorization, (iii) Semantic word extraction, (iv) Semantic similarity checking, (v) Feature extraction, and (vi) Classification. Accordingly, the Mongodb documented tweets initially underwent pre-processing with stop word removal, stemming, and blank space removal. Regarding the extracted keywords, the existing semantic words are derived after categorizing the sentiment of keywords. Additionally, the semantic similarity score is evaluated along with their keywords. The subsequent step is feature extraction, where the Holoentropy features such as cross Holoentropy and joint Holoentropy are formulated. Along with this, the extraction of weighted holoentropy features is the major work, where weight is multiplied with the holoentropy features. Moreover, in order to enhance the performance of classification results, the constant term utilized in evaluating the weight function is optimized. For this optimal tuning, a new, improved algorithm termed as Self Adaptive Moth Flame Optimization (SA-MFO) is introduced, which is the adaptive version of MFO algorithm. For classification, this paper aims to use the Deep Convolutional Neural network (DCNN), where the batch size is fine-tuned using the same SA-MFO algorithm. Finally, the performance of the proposed work is compared over other conventional models with respect to different performance measures.