International Journal of Pure and Applied Mathematics Volume 119 No. 15 2018, 235-241 ISSN: 1314-3395 (on-line version) url: http://www.acadpubl.eu/hub/ Special Issue http://www.acadpubl.eu/hub/ A SVM Based Sentiment Analysis Method (SBSAM) for Unigram and Bigram Tweets S. Geetha Dr. R. Kaniezhil Assistant Professor Principal Department of Computer Science MIT College of Arts & Science for Women Muthurangam Govt. Arts College(Autonomous) Musiri - 621211 Vellore- 632001 Email:[email protected] Email: [email protected] I. INTRODUCTION Abstract: Social media sites are places where citizens voice their opinions without fear. There is growing sense of Social media sites are places where citizens voice urgency to understand public opinions because of the viral their opinions without fear. There is growing sense of nature of social media. Making sense of these mass conversations for interacting meaningfully is in demand. urgency to understand citizens opinions because of the Sentiment analysis (SA) is the study where sentiments are viral nature of social media. Making sense of these computed for a conclusion. SA can apply anywhere as mass conversations for interacting meaningfully are public opinion on variety of subjects can be assessed. increasing in demand. Sentiment analysis is the study Stock markets fluctuations are in a way based on public where sentiments are computed for a conclusion. opinion. SA can help optimize promotional strategies. In Sentiments Entity can be classified as product, person, marketing tactics, sentiment analysis can help fit brand, event, concept. Analysis of sentiments is a marketing campaigns for target audiences. Success of a dynamic and challenging task [1]. This has created the campaign also lies in positive discussions amongst demand for Information filtering systems (IFS) dealing customers, where sentiment analysis plays a major role. Moreover, the volume of digital information on the with information overload (Recommender Systems) [2]. Internet has been responsible in increasing access times on Information retrieval systems have been able to satisfy items of interest for users. This voluminous information these analysis by prioritization or personalization of has to be filtered, prioritized and delivered to users to interesting information found on the internet, but are satisfy their search requirements for recommendations. scarce in numbers. These systems also called SA can help quality refinements and further research. recommender systems, filter vital information This paper underlines the need for sentimental analysis fragments according to user‟s preferences or interest or and recommender systems based on sentimental analysis behaviors from the dynamically generated internet for users. Further, it proposes a Sentiment Analysis information [3]. Recommender systems are beneficial Method based on SVM data mining technique (SBSAM) on unigram and bigram tweets. SBSAM aims to fulfill to service providers and users alike and can predict a sentimental analysis with speed for recommender systems user preferences on items based on their profile[4]. The useful to end users. systems help improve qualitative decision making processes [5]. Millions of users share their opinions on 235 International Journal of Pure and Applied Mathematics Special Issue various topics on social networking sites. The limit of randomly, start to plan strategically for promoting 140 characters for messages in these micro-blogging their products based on public feedbacks. sites forces the users to be concisely expressive in their Managerial viewpoints can based on the analysis of comments or opinions. customers chit chat about brands and used to post Twitter, a social media site has over two hundred full brand information to gauge how customers million users, where more than 50% are active. More perceive the product or brand. It is a place for than fifty percent if Twitter users log on generating optimization of marketing strategies. By listening more than 200 million tweets per day [6]. Public to customers feeling high level decisions can be sentiments reflect on tweets which can be analyzed. adjusted to meet customer needs. Tactical These expressed sentiments are vital to firms for marketing can be effected by building short-term finding out responses about their products or to marketing campaigns for customer requirements. politicians for predicting election results or investors By continuously having sentiment analysis in for predicting stock prices. Studies classified sentiments place, campaigns can be adjusted to fit more of the as positive and negative based on unigrams, features target audiences. and a tree kernel. Unigrams were analyzed for more Measuring ROI of Campaigns: Success of than hundred features and joint to a tree [7]. Tweet marketing campaign may be measured by the sentiment namely objective and subjective were increase in the number of followers or comments automatically identified and separated in [8]. In the or likes. The true success lies in positive second step subjective message‟s polarity was discussions amongst customers on campaigns. determined. Sentiments in linguistic features were Sentiment analysis can relate and count positive or studied in [9] and evaluated the usefulness of lexical negative discussions that have occurred amongst resources used in informal and creative texts of tweets. audiences. By combining quantitative and e Parts of speech were found to be less useful in qualitative measurements, the true ROI of analyzing micro-blogging tweets [10]. They extracted marketing campaigns can be measured. features from lexicons along with micro-blogging Developing product quality: Sentiment analysis features and classified automatically Twitter sentiments helps completion of market research by assessing The messages were classified as positive or negative. customer opinions on products/services and ways Their framework had two distinct components namely to align products/services. Products are judged by classifiers and feature extractors which achieved higher presentations like pricing and package design. accuracy with machine learning algorithms on Ideas on developing product quality and sentiment analysis. presentation can be derived directly from customer Thus, sentiment analysis from user tweets can be of opinions. Sentimental Analysis of opinions is an help in many areas. Manual extraction of such useful alternative to structured and planned surveys. It can information from this voluminous data is almost also be used to fulfill customer complaints. impossible. Sentiments can be categorized into positive Improving customer service: Customers who buy or negative or neutral which helps determine attitude of products try to be loyal to the brand as long as the general public on topics. This paper aims to detect possible and influence the products or brands sentiments from tweets as accurately as possible. The positively. Hence, it is important to have the best technique has three main parts, where preprocessing customer service in place for keeping current tweets text is the first part, in the second part, required customers happy. On-time delivery, response on features are extracted and in the final part machine social media and reimbursement for erroneous learning algorithm SVM is used for classification [11]. product are some of the cues for good customer Thus, this paper proposes a novel method for service. Sentiment analysis can identify negative sentimental analysis called, SBSAM based on SVM discussions and thus be an alert on improvements. classifier, for accurate and automatic sentiment analysis Faster responses draws customer attention and of twittered tweets. eventually their satisfaction. Sentiment analysis is a part of social complaint listener that helps avoid A. Benefits of Sentimental Analysis customers feeling ignored and angry like Finnair responding to customer‟s twitter. Sentimental Analysis can be used by celebrities, Crisis management: Constant monitoring of organizations, governments and individuals to get social media conversations also helps minimize public opinions voiced on social media. Some of the damage of crisis due to online communications. A major benefits of sentimental analysis is detailed below product's bad quality, environmental worries , bad Fine-tune marketing strategies: Many customer service can create a catastrophe in organization actively use social media to promote emerging markets. Sentiment analysis can detect their products. Even companies which post 236 International Journal of Pure and Applied Mathematics Special Issue manifestations from customer discussions and thus names, they are prone to poor results. Mostly names get help manage crisis early. misclassified. Lead generation: Accurate sentiment analysis can Identifying the user’s preferences: Sentiment result in better marketing campaigns and with analysis in political arena can forecast election customer service, and quality, it can result in new outcomes. Categorizing users into groups like left, right leads. Happy customers, act as brand ambassadors and independent and gaining information about users and bring in new customers. Also, customer needs, helps improve the social media-based predictions, but their views can give create a path for better selling are not too easy [17]. content that can attract new customers. Hashtags: Sentiment analysis on emotions and Sales Revenue: The biggest benefit of sentiment emoticons can identify emotions for monitoring [18]. analysis lies