The survey of sentiment and opinion mining for behavior analysis of social media <s> RELATED WORK <s> With the rapid growth of the Internet, users' ability to publish content has created active electronic communities that provide a wealth of product information. Consumers naturally gravitate to reading reviews in order to decide whether to buy a product. However, the high volume of reviews that are typically published for a single product makes it harder for individuals to locate the best reviews and understand the true underlying quality of a product based on the reviews. Similarly, the manufacturer of a product needs to identify the reviews that influence the customer base, and examine the content of these reviews. In this paper, we propose two ranking mechanisms for ranking product reviews: a consumer-oriented ranking mechanism ranks the reviews according to their expected helpfulness, and a manufacturer-oriented ranking mechanism ranks the reviews according to their expected effect on sales. Our ranking mechanism combines econometric analysis with text mining techniques in general, and with subjectivity analysis in particular. We show that subjectivity analysis can give useful clues about the helpfulness of a review and about its impact on sales. Our results can have several implications for the market design of online opinion forums. <s> BIB001 </s> The survey of sentiment and opinion mining for behavior analysis of social media <s> RELATED WORK <s> Directed links in social media could represent anything from intimate friendships to common interests, or even a passion for breaking news or celebrity gossip. Such directed links determine the flow of information and hence indicate a user's influence on others — a concept that is crucial in sociology and viral marketing. In this paper, using a large amount of data collected from Twitter, we present an in-depth comparison of three measures of influence: indegree, retweets, and mentions. Based on these measures, we investigate the dynamics of user influence across topics and time. We make several interesting observations. First, popular users who have high indegree are not necessarily influential in terms of spawning retweets or mentions. Second, most influential users can hold significant influence over a variety of topics. Third, influence is not gained spontaneously or accidentally, but through concerted effort such as limiting tweets to a single topic. We believe that these findings provide new insights for viral marketing and suggest that topological measures such as indegree alone reveals very little about the influence of a user. <s> BIB002 </s> The survey of sentiment and opinion mining for behavior analysis of social media <s> RELATED WORK <s> Recently, there has been tremendous interest in the phenomenon of influence propagation in social networks. The studies in this area assume they have as input to their problems a social graph with edges labeled with probabilities of influence between users. However, the question of where these probabilities come from or how they can be computed from real social network data has been largely ignored until now. Thus it is interesting to ask whether from a social graph and a log of actions by its users, one can build models of influence. This is the main problem attacked in this paper. In addition to proposing models and algorithms for learning the model parameters and for testing the learned models to make predictions, we also develop techniques for predicting the time by which a user may be expected to perform an action. We validate our ideas and techniques using the Flickr data set consisting of a social graph with 1.3M nodes, 40M edges, and an action log consisting of 35M tuples referring to 300K distinct actions. Beyond showing that there is genuine influence happening in a real social network, we show that our techniques have excellent prediction performance. <s> BIB003 </s> The survey of sentiment and opinion mining for behavior analysis of social media <s> RELATED WORK <s> The proliferation of social media is opening up new possibilities for companies to increase their awareness of openly expressed consumer sentiment about themselves and their competitors. In this paper a wide variety of software tools designed for monitoring social media are reviewed. Definitions for relevant concepts are given. Several tool characteristics are evaluated including analyzable social media types and language support. The main finding is that the tools offer a wide coverage of social media, but vary significantly in other characteristics with notable room for improvements. Therefore, careful judgment should be exercised when selecting the right tool for a particular use. <s> BIB004 </s> The survey of sentiment and opinion mining for behavior analysis of social media <s> RELATED WORK <s> Users are sharing vast amounts of social data through social networking platforms accessible by Web and increasingly via mobile devices. This opens an exciting opportunity to extract social perceptions as well as obtain insights relevant to events around us. We discuss the significant need and opportunity for analyzing event-centric user generated content on social networks, present some of the technical challenges and our approach to address them. This includes aggregating social data related to events of interest, along with Web resources (news, Wikipedia pages, multimedia) related to an event of interest, and supporting analysis along spatial, temporal, thematic, and sentiment dimensions. The system is unique in its support for user generated content in developed countries where Twitter is popular, as well as in support for SMS that is popular in emerging regions. <s> BIB005 </s> The survey of sentiment and opinion mining for behavior analysis of social media <s> RELATED WORK <s> This paper focuses on the problem of identifying influential users of micro-blogging services. Twitter, one of the most notable micro-blogging services, employs a social-networking model called "following", in which each user can choose who she wants to "follow" to receive tweets from without requiring the latter to give permission first. In a dataset prepared for this study, it is observed that (1) 72.4% of the users in Twitter follow more than 80% of their followers, and (2) 80.5% of the users have 80% of users they are following follow them back. Our study reveals that the presence of "reciprocity" can be explained by phenomenon of homophily. Based on this finding, TwitterRank, an extension of PageRank algorithm, is proposed to measure the influence of users in Twitter. TwitterRank measures the influence taking both the topical similarity between users and the link structure into account. Experimental results show that TwitterRank outperforms the one Twitter currently uses and other related algorithms, including the original PageRank and Topic-sensitive PageRank. <s> BIB006 </s> The survey of sentiment and opinion mining for behavior analysis of social media <s> RELATED WORK <s> In Chap. 9, we studied the extraction of structured data from Web pages. The Web also contains a huge amount of information in unstructured texts. Analyzing these texts is of great importance as well and perhaps even more important than extracting structured data because of the sheer volume of valuable information of almost any imaginable type contained in text. In this chapter, we only focus on mining opinions which indicate positive or negative sentiments. The task is technically challenging and practically very useful. For example, businesses always want to find public or consumer opinions on their products and services. Potential customers also want to know the opinions of existing users before they use a service or purchase a product. <s> BIB007 </s> The survey of sentiment and opinion mining for behavior analysis of social media <s> RELATED WORK <s> The microblogging site Twitter generates a constant stream of communication, some of which concerns events of general interest. An analysis of Twitter may, therefore, give insights into why particular events resonate with the population. This article reports a study of a month of English Twitter posts, assessing whether popular events are typically associated with increases in sentiment strength, as seems intuitively likely. Using the top 30 events, determined by a measure of relative increase in (general) term usage, the results give strong evidence that popular events are normally associated with increases in negative sentiment strength and some evidence that peaks of interest in events have stronger positive sentiment than the time before the peak. It seems that many positive events, such as the Oscars, are capable of generating increased negative sentiment in reaction to them. Nevertheless, the surprisingly small average change in sentiment associated with popular events (typically 1% and only 6% for Tiger Woods' confessions) is consistent with events affording posters opportunities to satisfy pre-existing personal goals more often than eliciting instinctive reactions. © 2011 Wiley Periodicals, Inc. <s> BIB008 </s> The survey of sentiment and opinion mining for behavior analysis of social media <s> RELATED WORK <s> important part to gather the information is always seems as what the people think. The growing availability of opinion rich resources like online review sites and blogs arises as people can easily seek out and understand the opinions of others. Users express their views and opinions regarding products and services. These opinions are subjective information which represents user's sentiments, feelings or appraisal related to the same. The concept of opinion is very broad. In this paper we focus on the Classification of opinion mining techniques that conveys user's opinion i.e. positive or negative at various levels. The precise method for predicting opinions enable us, to extract sentiments from the web and foretell online customer's preferences, which could prove valuable for marketing research. Much of the research work had been done on the processing of opinions or sentiments recently because opinions are so important that whenever we need to make a decision we want to know others' opinions. This opinion is not only important for a user but is also useful for an organization. KeywordsMining; Machine learning; Sentiments; Polarity; semantic. <s> BIB009 </s> The survey of sentiment and opinion mining for behavior analysis of social media <s> RELATED WORK <s> Abstract Social media (i.e., internet applications and platforms that allow users to create and share content) are a potentially rich data source for foodborne disease surveillance. This commentary qualitatively evaluates social media as a foodborne disease surveillance system component and discusses novel ways that social media may facilitate the detection of intentional and unintentional food contamination. <s> BIB010 </s> The survey of sentiment and opinion mining for behavior analysis of social media <s> RELATED WORK <s> Food crises imply responses that are not what people and organisations would normally do, if one or more threats (health, economic, etc.) were not present. At an individual level, this motivates individuals to implement coping strategies aimed at adaptation to the threat that has been presented, as well as the reduction of stressful experiences. In this regard, microblogging channels such as Twitter emerge as a valuable resource to access individuals' expressions of coping. Accordingly, Twitter expressions are generally more natural, spontaneous and heterogeneous - in cognitive, affective and behavioural dimensions - than expressions found on other types of social media (e.g. blogs). Moreover, as a social media channel, it provides access not only to an individual but also to a social level of analysis, i.e. a psychosocial media analysis. To show the potential in this regard, our study analysed Twitter messages produced by individuals during the 2011 EHEC/Escherichia coli bacteria outbreak in Europe, due to contaminated food products. This involved more than 3100 cases of bloody diarrhoea and 850 of haemolytic uraemic syndrome (HUS), and 53 confirmed deaths across the EU. Based on data collected in Spain, the country initially thought to be the source of the outbreak, an initial quantitative analysis considered 11,411 tweets, of which 2099 were further analysed through a qualitative content analysis. This aimed at identifying (1) the ways of coping expressed during the crisis; and (2) how uncertainty about the contaminated product, expressed through hazard notifications, influenced the former. Results revealed coping expressions as being dynamic, flexible and social, with a predominance of accommodation, information seeking and opposition (e.g. anger) strategies. The latter were more likely during a period of uncertainty, with the opposite being true for strategies relying on the identification of the contaminated product (e.g. avoid consumption/purchase). Implications for food crisis communication and monitoring systems are discussed. <s> BIB011 </s> The survey of sentiment and opinion mining for behavior analysis of social media <s> RELATED WORK <s> In this position paper we present the scenario of making interventions for increasing the classical music concert-going behaviour of end users. Within the FP7 Phenicx project we are developing a personalized persuasive system that attempts at changing the concert-going behaviour of users. The system is based on the theory of planned behaviour user model for predicting whether a user will attend a concert or not. Our goal is to develop a machine learning algorithm that will extract the user model parameters unobtrusively from the micro-blogs of the users. We plan to perform a user study to build the training dataset and to test the system on real users within the project. <s> BIB012 </s> The survey of sentiment and opinion mining for behavior analysis of social media <s> RELATED WORK <s> It is increasingly apparent that social media is a unique and important resource to explore spontaneous relations and discourses among individuals. Data analysis in social media, however, is difficult because approaches have not been streamlined. In this paper, we move from the discussion of present perspectives in the analysis of social media to then discuss the methodological strategies available and their limits. We finally discuss the value of choosing a mixed methods approach to the analysis of social media, exemplifying its heuristic contribution by a real research case. <s> BIB013
Nidhi Mishra et al., 2012has described the importance of opinion and sentiments mining. Opinion mining is distributed into four sub-categories: sentence level, document level, feature level and compound level respectively, and has discussed the various tools and mining methods like precision, recall and F-measure on movie reviews or product reviews. Aspect level classification can provide greater detail for analysis then document and sentence level. The distribution categories miss classification subjective expressions and objective expressions for better understanding of sentiments BIB009  BIB007 . Meeyoung Cha et al., 2010investigated the dynamics of public influence across different issues / topics to determine the influence author used in degree, retweets and mentions approaches on a large dataset of twitter. Spearman's rank correlation coefficient has been used for evaluating the influence of people against three different topics. Author concludes the result in terms of influence against each topic but he didn't target the fake users which can actively participate in discussions. Fake user can play the major role to divert the influence of public from one issue to another. BIB002 BIB003 . Guendalinaet al., 2015presents a new approach known as the integrated mixed methods approach (IMiME) for monitoring the content of social media by demonstrating a case study 'hypoactive sexual desire disorder (HSDD). Integrated Mixed methods approach (IMiME) is the combination of qualitative and quantitative approaches. Although author has presented the result of IMiME approach as balanced between ecological and situated nature of qualitative research strategies with systematization of quantitative measurement. This approach have to be tested with other case studies for concrete satisfaction BIB013 BIB004  . Rui Gaspar et al., 2014has assessed the ways of coping, expressed by public in social media (Twitter) during food crises for its management and prevention. Methodological, conceptual limitation and challenges are discussed for coping analysis and how to apply psychosocial process to cope the content of single tweet. Qualitative approach is used for cope analysis. Qualitative approach is much hard and time consuming to analyze large dataset. Author selected limited data after cleansing for better results. During selection of data, author didn't consider the spam opinions. Spam opinions can be more harmful for decision making. Qualitative approach is used, if the Quantitative approach may also be added than it will be helpful for coping analysis and supportive for decision making BIB011 BIB010 BIB005 BIB008 . JianshuWenget al., 2010 explored the most influenced users of Twitter (micro-blogging site) by homophily. In homophily, a single user choose and follow a friend on twitter because he is interested in the topics which were published in tweets by the friend and friend follows back because he observes that they both have similar interest topics. Author endorse the homophily is not being used in case of twitter by using TwitterRank Approach. TwitterRank approach is the combination of Latent Dirichlet Allocation (LDA) and TwitterRank algorithms. Topic-sensitive PageRank has also been discussed, and its comparison with TwitterRank has been put forth. A group of spam/fake users can add someone to become more influenced user like celebrities, politicians etc. The author didn't detect the group of fake users as optimistic approach BIB006  . Federico Neriet al., 2012has applied the sentiment mining analysis on Facebook textual dataset for news channel by using Recall and Precision approach. In this approach, it is very difficult to identify what is relevant to one person might not be relevant to another person and vice versa. Naïve Bayes can be used instead of Precision and Recall for better results   .( Jayasankaet al., 2014has assessed the challenges which were observed during extraction of expressions of optimistic or pessimistic attitudes through social media and has also discussed the different available tools and their limitations for sentiment analysis. Naïve Bayes classification method which is used in order to produce better results of sentiment mining instead of Natural language Processing and SentiWordNet. Scores are used in order to project the results of negative and positive sentiments. Expectation-Maximization (EM) algorithm can also be used in order to optimize the accuracy as the SentiWordNet does not make much difference among short sentences like 'not good' and 'not bad'   . ( Ion SMEUREANU et al., 2012has presented a work for exploring the positive and negative opinions on pre-classified movies' reviews. Naïve Bayes Algorithm was applied on collection of comments. Precision and recall methods are used for accuracy check of Naïve Bayes algorithm and its execution time   . Tkalcicet al., 2014has presented the scenario and design of personalized intervention system (PIS) and has chosen theory of planned behavior (TPB) method to predict whether user will attend the classical musical concert or not through social media mining. As theory of planned behavior (TPB) does not consider the emotional elements like feelings, mode; emotion element which has direct effect on personalized intervention system (PIS) predictions is not considered BIB012 . Praveshet al., 2014has assessed and evaluated the numerous methods which are used for opinion and sentiment. Naïve Bays Classifier, Support Vector Machine (SVM), Multilayer Perceptron, Clustering techniques has been used in order to analyze and compare the results of each technique. Every technique offer various benefits and boundaries, each method can be utilized according to the situation for features and text extractions. There are many other methods which can be utilized for extraction of sentiments from micro-blogging sites  . Anindya BIB001 has proposed two ranking mechanisms: consumer-oriented ranking, manufacturer-oriented mechanism. These mechanisms are consisted on econometric and subjectively analysis and text mining. Consumer-oriented mechanism is used in order to rank the reviews according to their expected helpfulness while manufacturer-oriented mechanism is used in order to rank the reviews according to their expected effect on sales. Reviews can be spam or genuine but in these mechanism no spam detection method has been used. If spam reviews are also included in the ranking system, it will spoil the results and detract the community BIB001 . 