Sentiment Analysis And Stock Market Prediction

Here we discuss about the common traits or ideas observed in the three research topics. Although, these three papers discuss about different ideas, they all fall under the web data mining domain. web data mining is a hot research topic in the current realm of big data. These papers discuss about the utilisation of the valuable user generated data from the social media or the the browser cookies to provide the best user experience in order to maintain the user interest in the company's product or to take effective decisions by the individual.

In this paper we will be looking at search engines, social media and blogging and try and way up

Opinion mining (also known as sentiment analysis) focuses mainly on two things, first identifying whether the text is subjective or objective and then determining the polarity of the subject [3]. Opinion words in the text mostly contribute towards the subjectivity of the text. These opinion words together inform about the emotional context of the text. Objective words contribute towards the factual construct of the text. There are three most common approaches used for determining the sentimental content of a text

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

As in our study, LDA topics has improved accuracy of finding the keywords for different topics.In this work we examine the social aspects of food tweeting behavior, and provide some support to the social affinity that is not local in geographic sense. There have been several recent studies that probe the viability of public health surveillance by measuring relevant textual signals in social media.Prier, K.W.Smith, M.S.Giraud-Carrier, C. L. Hanson[5] examine all words people use in online reviews, and draw insights on correlating terms and concepts that may not seem immediately relevant to the hygiene status of restaurants. The work draws from the rich body of research that studies online reviews for sentiment analysis based on few research papers.

The stock market movements are complex and difficult to predict with certainty. Strategies tend to work for a while before they fail terribly. Mathematical and statistical formulae don’t hold for long. This is mainly because some of the factors influencing the demand/supply of a stock cannot be quantified and may be poison distributed. Historical patterns are broken every now and then while fundamental data bears different effects on stocks at different times of an economic cycle. However, there appears to be a correlation between the stock market movements and the atmosphere in the social media. This is yet to be tested in this study, even though there are numerous studies concerning this topic. However, through the AP’s Twitter account,

Sentiment analysis concentrates on attitudes, whereas the traditional method of text mining mainly focuses on the analysis of facts. There are few main fields of research in Sentiment analysis: sentiment classification, feature based sentiment classification and opinion summarization. Sentiment classification deals with classifying entire document according to the opinions shown with respect to a certain object. While feature-based sentiment classification considers the opinions on the features of a certain object. Opinion summarization task is different compared to traditional text summarization because only the features of the product are examined on which the customers have expressed their opinions via any social media. Opinion summarization does not summarize the reviews by selecting a subset or rewriting some of the original sentences from the reviews to capture the main points like in the classic text summarization

In the sentiment extraction phase, the candidate’s name extracted from the resume was used to search on Twitter to find his/her profile. The candidate’s tweets are retrieved for further processing using Tweepy package and its API [13]. Then, these tweets are analysed to extract the sentiments for each candidate. This is done by using Textblob package which is a popular natural language processer [14].

Opinion mining and sentiment analysis is discussed in chapter 7 of Amy Van Looy’s Social Media Management; Technologies and Strategies for Creating Business Value (pp.133-147) “Opinion mining and sentiment analysis can be defined as processing “a set of search results for a given item,

First, we derive the collective opinion $O$ of a whole data sample $D$, using a SA method existing in the literature. Specifically, we adopt in this work the method SACI cite{jws2015}. SACI is relevant to our goal since it was originally proposed for estimating efficiently collective sentiment on data samples, instead of aggregating the sentiment derived for each individual document. Further, the authors demonstrated that SACI is more effective in estimating the collective opinion than aggregation-based SA methods. SACI represents $O$ as a distribution probability among the sentiment classes positive, negative and neutral. Thus, we split $D$ into time units of equal size (e.g., days, weeks, months). Then, we estimate the collective opinion $O_t$ using only the posts belonging to each distinct time unit $t$. Finally, we perform a visual inspection on the derived distributions. The more dynamic a domain, the more different are opinions estimated on distinct time units.looseness=-1

The following section provides a literature review on the role of social media within the online community. Next, an overview of sentiment analysis is discussed, twitter sentiment analysis, the application of sentiment analysis in political discussions, sentiment analysis techniques, the features of sentiment analysis and finally the challenges attached to sentiment analysis.

In the first phase, the extraction patterns are applied on a large corpus of text obtained from twitter to yield a set of subjective terms. In the second phase, the extracted terms, are assigned polarity based on the normalized point wise mutual information score between them and positive and negative terms derived from an existing polarity lexicon. Details of the process are presented in the following subsections.

Twitter is, without question, one of the most active, popular social platforms on the web.

IndexTerms— Location estimation, Traffic congestion, Tweet classification, Text mining, Twitter, Naïve Bayes Classifier, Real-time event detection.

Sentiment analysis or opinion mining is an emerging area of research, because, the impact of the web is increasing at a very fast rate, now most of the people would like to share their opinions, feelings and experiences on the web.. Now people commonly use blogs, forums, e-news, reviews channels and the social networking platforms such as