Location Based Sentiment Analysis Of Twitter Data: A Literature

In the paper Nguyen et al \cite{Micro:Ng}, the model makes use of the Micro-reviews\cite{Micro:Ng} generated by the users through various social media sites about any particular entity. Micro-Reviews \cite{Micro:Ng} are the reviews that are not too long , easy to comprehend and also considered as the most appropriate feedback of the customer. But it is starting to get complicated as the number of Micro-Reviews \cite{Micro:Ng} are increasing and is hard to go through several thousands of the user reviews to find the best review suitable to the user preferences. In order to overcome this, these reviews are categorised in to either positive or negative feedbacks. Then this Micro-Reviews \cite{Micro:Ng} are associated and

The paper will began by an overview of the social media sector accompanied by its impact on human behaviour. I will then talk a bit about the modern marketing strategies which companies are using through social media. Later on I will describe the Statistics that Show Social Media is the Future of Customer Service. Finally the essay will conclude with my opinion about the impact of social media on today’s business world and human life.

Social media: new platform for intelligence, help business identify customer’s view about performance and customer relationship of business in the market.

The posts that are tweeted in the platform can be predicted through the use of machine learning technique. In context, the aforementioned works on the scale of predicting a tweet given the content of the tweet, the tweeter and more especially the retweeted. The above factors are instrumental in developing a detailed and analyzed strategy of acquiring information through Twitter. Notable also is the fact that the popularity of a user does not depend on the number of followers that one has or the count of the tweets. However, the count of the retweets and the number of users who took part in the process act as the appraisal of popularity and how quick the information will be propagated in the network. The factors that limit the propagation of the information in Twitter is the limit of the word character which is only 140. As such, there is a need to have a predefined terse message that will enhance the spread of the information. There is also need to authenticate information in Twitter so as to hamper rumors and

In “An Examination of the Factors Influencing Consumers ' Attitudes Toward Social Media Marketing.?” Akar and Topcu point out that social media has become a phenomenon in marketing. (Akar and Topcu, 2011) Marketers are beginning to understand the use of social media as a component in their marketing and strategies and campaigns to reach out to customers. Promotions marketing intelligence, sentiment research, public relations, marketing communications, and product and customer

Big data is buzzword in every field of business as well as research. Organizations have found its application across various sectors from Sports to Security, from Healthcare to e-Commerce.

It is used to understand the emotion conveyed in a textual message. It involves identifying the opinion, extracting the features or objects for which the opinion is expressed and then categorizing the opinion as a positive, negative or neutral and thus assigning it a polarity (Liu 2010). The growth in social media provides a wider platform which has allowed for an abundance in the expression of opinions, including product reviews, blogs, and discussion groups or simply as comments and tweets. Different techniques for sentiment analysis use Natural Language processing and machine learning perform Sentiment analysis on the large quantities of data available on the social media networks.

With hundreds of millions of people sending countless hours on social media to share, communicate, connect, interact, and create user-generated data at an unprecedented rate, social media has become one unique source of big data.” The focus of the research problem under Social Media mining will be the sentimental analysis/political trends because of the sheer amount of data available on social media sites with people posting about politics.

Social media has become prominently popular. Tens of millions of users login to social media sites like Twitter to disseminate breaking news and share their opinions and thoughts. For businesses, social media is potentially useful for monitoring the public perception and the social reputation of companies and products. Despite great potential, how bad news about a company influences the public sentiments in social media has not been studied in depth. The aim of this study is to assess people’s sentiments in Twitter upon the spread of two types of information: corporate bad news and a CEO’s apology. We attempted to understand how sentiments on corporate bad news propagate in Twitter and whether any social network feature facilitates its

Positive emotions are the experience of positive moods and feelings which are uplifting. Positive emotions serve as a proof of flourishing and optimal wellbeing in a people's lives. The moments which are filled with positive emotions such as joy, happiness, hope, confidence, love- are the moments in which the negative emotions such as anxiety, envy, sadness, and anger has no place.

Yang Peng, Melody Moh, Teng-Sheng Moh, Efficient Ad- verse Drug Event Extraction using Twitter Sentiment Analysis , in this they proposed a simple, efficient pipeline for retrieving ADEs. Any selected drug should have been in the market for more than ten years. Following this rule, there are sufficient number of tweets exist for any selected drug. Drug related classification is done on preprocessed Data. Sentimental Anal- ysis. 5 times

The approach used in this thesis is inspired by Bollen et al’s strategy [12], with a step taken forward to implement PageRank algorithm to increase the accuracy of results and use of different sentiment analysis techniques than the techniques used by him. In 2010, Bollen used Twitter data for finding the predictability of Twitter sentiments on stock market with high accuracy. He proposed a method for prediction of the changes in the stock market price based on the mood of people on Twitter.

Big data is an extensive collection of structured and unstructured data. It is a modern day technology which is applied to store, manage and analyze data that are not possible to manage, store and analyze by using the commonly used software or tools. Since all of our daily tasks are overtaken by the modern technologies and all the businesses and organizations are using internet system to operate, the production of data has increased significantly in past

Some combined rule algorithms were proposed in (Medhat et al., 2008a). Therefore, a study on decision tree and decision rule problem is done by Quinlan (1986). Probabilistic Classifier Probabilistic classifiers make the utilization of blend of models for classification. Every class is considered to be a component of the mixed model. We have described various probabilistic classifiers for sentiment analysis problem in the next subsection. 4.1.1.4.1 Naive Bayes Classifier (NB). It is the frequently used classifier in sentiment analysis. In sentiment analysis, naive Bayes classifier calculates the posterior probability of either positive class or negative class depending on the sentiment words distributed over the document. The work of naïve Bayes classifier is based on the Bag-of-word extraction of features in which the word’s position is overlooked in the whole text. This classifier uses the Bayes theorem. It calculates the probability for the sentiment word in a document and tells whether that word belongs to the positive or negative class. The probability can be calculated using the given formula. This assumption results in Bayesian Network. A Bayesian network is a directed acyclic graph containing nodes and edges, where nodes denote the random variables and the edges denote the conditional dependencies. It is a conditional exponential classifier that takes the feature sets with label and converts them into

Sentiment analysis consists of various elements out of which lexicons are an integral part. These lexicons are nothing but the words which make up a sentence. These lexicons can also be known as sentiment lexicon or opinion lexicons. It is important that we classify the lexicons we have into positives or negatives in order to achieve review analysis. One of the methods for sentiment analysis and classification is done on the basis of these lexicons. There are two broad approaches for sentiment analysis. These are lexicon based approaches and machine learning based approaches.