Academic journal article Informatica Economica

The Power of Social Media Analytics: Text Analytics Based on Sentiment Analysis and Word Clouds on R

Academic journal article Informatica Economica

The Power of Social Media Analytics: Text Analytics Based on Sentiment Analysis and Word Clouds on R

Article excerpt

Introduction

Microblogging or Social media sites have developed to wind up plainly a source of fluctuated sort of data. This is because of nature of social media on which individuals post constant messages about their opinion on an assortment of points, examine current issues, grumble, and express positive feeling for items they use in day by day life. Actually, organizations assembling or manufacturing such items have begun to survey these microblogs or social media to get a perception of general slant for sentiment for their items or product. Commonly these companies think about client responses and answer to clients on social media. One test is to develop technology to distinguish and outline a general sentiment.

While there has been a considerable amount of research on how assumptions are communicated in genres, for example, online reviews, blogs and news articles, how feelings or sentiments are communicated given the casual language and message-length requirements of microblogging or social networking has been substantially less studied. Highlights, for example, programmed grammatical form labels and resources, for example, sentiment vocabularies have demonstrated valuable for sentiment examination or analysis in different areas, yet will they likewise demonstrate helpful for sentiment analysis in twitter? In this paper, we start to explore this question. Word clouds produced for a collection of text can fill in as a beginning stage for a more profound analysis [1-3]. For example, they help to decide whether a given text is applicable to a particular data require. One of their downsides is that they give a simply factual rundown of disengaged words without considering phonetic information about the words and their relations. Subsequently, word clouds are utilized rather statically as a way to outline message in many frameworks and they regularly give no or just restricted collaboration abilities. We think there is a bigger potential to this straightforward yet capable visualization worldview in numerous analyzing con- texts. In this work, we, along these lines, investigate the potential outcomes by utilizing word clouds at the exceptionally focal point of text mining.

In this paper, we analyze one such prevalent microblog or social media called twitter and build R models for characterizing "tweets" into positive, negative and unbiased sentiment and also create word cloud to find out the most frequently used term. For twitter sentiment, we assemble models for twitter authentication, and then we will pull the data from twitter. Here we will use a political figure to analyze sentiment what type of words are being used by him in everyday life to figure out actually what is happening in his mind. By using the R models, we will basically create a graph of positive, negative and neutral words used by the twitter user.

To generate word cloud, we will first use R model to authenticate twitter. Then we will pull twitter data of a famous phone company. Then we will process the twitter data in a way that we can create a word cloud based on the dataset. The finalized word cloud will picture what the company is actually thinking. Meaning which words are being used frequently on this particular twitter account.

2 Managerial Contribution

In general, social media analytics are used for future forecasting for better business decision making and planning. In this study, we obtained and modified numerous source codes from different sources and run the algorithms by rendering a new technique to generate twitter sentiment and word cloud for further forecasting on political context. Although some of the results are already prevailing in scattered manner - we tried to put those in place - to illustrate the whole process of text mining for business analytics professionals or researchers.

3 Literature Review

Sentiment analysis is a developing area of Natural Language Processing with research extending from document level characterization [4] to taking in the extremity of words and phrases [5, 6]. …

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