Improving Effectiveness and Efficiency of Sentiment Analysis

Improving Effectiveness and Efficiency of Sentiment AnalysisModha Jalaj S.Chapter 11. Introduction macroscopic selective in versionation has been created lot of buzz in nurture engineering word. greathearted Data contain cosmic amount of information from various sources like accessible Media, parole Articles, Blogs, Web, Sensor Data and Medical Records etc.Big Data includes Structured, Semi-Structured and Unstructured data. All these data are very rehearseful to extract the important information for analytics.1.1 Introduction of Big Data 26Big Data is differs for other data in 5 Dimensions such as volume, velocity, variety, and value. 26Volume Machine generated data go forth be large volume of data.Velocity Social media websites generates large data except not massive. Rate at which data acquired from the social web sites are increasing rapidly.Variety Different types of data will be generated when a new sensor and new services.Value Even the unstructured data has some valu able information. So extracting such information from large volume of data is more considerable.Complexity Connection and correlation of data which describes more almost relationship among the data.Big Data include social media, proceeds reviews, photo reviews, News Article, Blogs etc.. So, to analyze this kind of unstructured data is challenging task.This thing makes Big Data a trending research area in reckoner Science and sentiment epitome is one of the most important part of this research area.As we have lot of amount of data which is certainly express opinion about the Social issues, events, organization, movies and News which we are considering for sentiment epitome and predict the future trends and effect of certain event on society.We send word besides modify or make the improve strategy for CRM after analysing the comments or reviews of the customer. This kind analysis is the application of Big Data.1.2 Introduction of Sentiment AnalysisBig Data is trending researc h area in computer Science and sentiment analysis is one of the most important part of this research area. Big data is considered as very large amount of data which faecal matter be found easily on web, Social media, remote sensing data and medical exam records etc. in form of structured, semi-structured or unstructured data and we can use these data for sentiment analysis.Sentimental Analysis is all about to catch the real voice of people towards specific product, services, organization, movies, news, events, issues and their attributes1. Sentiment Analysis includes branches of computer science like Natural Language Processing, Machine Learning, Text Mining and Information Theory and Coding. By using approaches, methods, techniques and models of defined branches, we can categorized our data which is unstructured data may be in form of news articles, blogs, tweets, movie reviews, product reviews etc. into positive, negative or neutral sentiment according to the sentiment is expre ssed in them.Figure 1.2.1 Sentiment AnalysisSentiment analysis is done on three levels 1Document LevelSentence LevelEntity or Aspect Level.Document Level Sentiment analysis is performed for the whole document and then decide whether the document express positive or negative sentiment. 1Entity or Aspect Level sentiment analysis performs finer-grained analysis. The goal of entity or horizon level sentiment analysis is to find sentiment on entities and/or expression of those entities.For example consider a statement My HTC Wildfire S phone has strong picture quality but it has low phone memory storage. so sentiment on HTCs camera and display quality is positive but the sentiment on its phone memory storage is negative. We can generate summery of opinions about entities. Comparative statements are also part of the entity or aspect level sentiment analysis but deal with techniques of comparative sentiment analysis.Sentence level sentiment analysis is related to find sentiment form se ntences whether each sentence expressed a positive, negative or neutral sentiment. Sentence level sentiment analysis is closely related to subjectivity classification. some of the statements about entities are factual in nature and yet they still carry sentiment. Current sentiment analysis approaches express the sentiment of subjective statements and give way such objective statements that carry sentiment 1.For Example, I bought a Motorola phone two weeks ago. Everything was good initially. The voice was clear and the battery life was long, although it is a combat bulky. Then, it stopped meeting yesterday. 1 The first sentence expresses no opinion as it simply states a fact. All other sentences express either explicit or unvoiced sentiments. The last sentence Then, it stopped working yesterday is objective sentences but current techniques cant express sentiment for the above stipulate sentence even though it carry negative sentiment or undesirable sentiment. So I try to solve o ut the above problematical situation using our approach. 1The Proposed classification approach handles the subjective as well as objective sentences and generate sentiment form them.1.3 ObjectivesThe objective of this research work is to improve the effectiveness and efficiency of classification as well as sentiment analysis because this analysis plays a very important role in analytics application.Till now Sentiment analysis focus on Subjectivity or Subjective sentiment i.e. explicit opinion and get idea about the people sentiment view on particular event, issue and products. Sentiment analysis does not consider objective statements although objective statements carry sentiment i.e. implicit opinion.So here the main objective is to handle subjective sentences as well as objective sentences and give better result of sentiment analysis.smorgasbord of unstructured data and analysis of classified unstructured data are major objectives of me.Practical implementation will be also done b y me in the next phase.1.4 ScopeScope of this dissertation is described as below.We are considering implicit and explicit opinion so sentiment analysis expect to be improvedAnalysis of unstructured data gives us important information about people choice and viewWe are proposed an approach which can be applied for close domain like Indian Political news article, Movie reviews, Stock Market News and Product Review so, with the consideration of implicit and explicit opinions we can generate precise view of people so industries can define their strategies.Business and Social Intelligence applications use this sentiment analysis so with this approach itll be efficient.ApplicationsThere are so many application of Sentiment Analysis which is used now-a-day to generate predictive analysis for unstructured data.Areas of applications are Social and Business intelligence applications, Product reviews help us to define marketing or production strategies, Movie reviews analysis, News Analysis, Consider political news and comments of people and generate the analysis of election, Predict the effect of specific events or issues on people, Emotional identification of person can be also generated, Find trends in the world Comparative view can also be described for products, movies and events, Improve predictive analysis of bring around of investment strategies.1.6 ChallengesThere are following challenges which are exists in sentiment analysis areDeal with noisy school text in sentiment analysis is difficult.Create SentiWordNet for open domain is challenging task i.e. make a universal SentiWordNet is the Challenging task.When a document discusses several entities, it is crucial to attain the text relevant to each entity. Current accuracy in identifying the relevant text is far from satisfactory.5There is a need for better modelling of compositional sentiment. At the sentence level, this means more accurate calculation of the overall sentence sentiment of the sentiment-beari ng words, the sentiment shifters, and the sentence structure. 5There are some approaches that use to identify sarcasm, they are not yet integrated within autonomous sentiment analysis systems.5