COMPREHENSIVE ANALYSIS OF TWITTER TRENDING
ABSTARCT :
Twitter is among the most used microblogging and online social networking services. In Twitter, a name, phrase, or topic that is mentioned at a greater rate than others is called a "trending topic" or simply “trend”. Twitter trends has shown their powerful ability in many public events, elections and market changes. Nevertheless, there has been very few works focusing on understanding the dynamics of these trending topics. In this article, we thoroughly examined the Twitter’s trending topics of 2018. To this end, we accessed Twitter’s trends API for the full year of 2018, and devised six criteria to evaluate our dataset. These six criteria are: lexical analysis, time to reach, trend reoccurrence, trending time, tweets count, and language analysis. In addition to providing general statistics and top trending topics regarding each criterion, we computed several distributions that explain this bulk of data.
EXISTING SYSTEM :
? One of the interesting features of Twitter is the existence of Trending Topics, which is a list of top ten most tweeted topics ranked by Twitter’s proprietary algorithm (‘Tweeting’ is a term for writing Twitter messages).
? There exists a (non-exhaustive) listing of categories used by studies on social awareness.
? The outcomes of these analyses may be used by several applications, such as event monitoring, and opinion mining about products or brands.
DISADVANTAGE :
? The purpose of this research is to analyze the views and sentiments of Twitter users on top trending social issues of the world in a specific time frame.
? In this work, an attempt is made to the mine tweets, capture the political sentiments from it and model it as a supervised learning problem.
? Our findings show that social media like Twitter helps to establish a sound perception of the social, political and cultural issues by analyzing the thoughts and feelings of people concerning their comments.
PROPOSED SYSTEM :
• In this paper, we propose model which use machine learning algorithm and classify sentiment of twitter message.
• It’s reasonable to assume that during a campaign, parties will use Twitter to advertise their candidates, propose party policies, participate in debates and interviews, and also criticize their opponents.
• We proposed three main reasons in the previous section, two of them may be the reasons for trend rising, and one may be the reason for trend falling.
ADVANTAGE :
? Sentiment analysis has now become an important source in decision-making. People depend upon it for future predictions and efficient working, yet we cannot say it is more than enough to rely upon its judgments because opinions and perceptions of people vary with the circumstances.
? They have used one dataset with three algorithms and performance has been evaluated on the basis three different information retrieval metrics precision, recall, and f-measure.
? In the proposed supervised learning techniques to classify twitter trending topic for that they use text based and network based classifier and conclude C5.0 gave best performance.
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