early prediction of athletes performance on the basis of twitter sentiment analysis8338

Project TitleProject Area of Specialization Computer ScienceProject Summary

The mood analysis is important to either team or manager, however, acquiring and using players’ moods from tweets is not straightforward because tweets take the form of unstructured text and too much noise is hiding in them. We need tools to analyze a sea of textual data in order to distill the hidden intelligence. we recommend to team operators that incorporates players’ tweets into their administrative decision-making process. It supports listening to and analyzing the tweets made public by Sport players which could help the operators of sport teams know players’ mental status (e.g., negative moods) and analysis players’ potential sport performance ahead, and then introduce necessary interventions. In this framework, sport players post tweets as reactions to a number of things such as feelings about a game, thoughts about coaches’ decisions, or more frequently life encounters. At the core of our recommendation is an emerging text-mining technique–sentiment analysis– that could be employed to sift through players’ tweets and analysis players’ moods before games. The results are then summarized as a report which is later presented to the operation staff of sport teams. Given the robust mood-performance association in the context of sports team operators could draw upon necessary approaches that are being practiced to smooth players’ negative moods or evoke positive moods. Operators of sport teams such as coaching staffs and the general managers could use Twitter to discern players’ status when they are not on the sports, particularly their moods, and to use the information that is otherwise difficult to obtain to predict their performance in the upcoming games.

Project Objectives

The main objective of our project is:

• Improve the performance of sport players.
• To analysis sport players tweet details.
• It also helps us to determine the player reviews of every game.
• To generate final report.

Project Implementation Method

Fetching Twitter Data using Twitter API

• The Twitter API directly communicates with the Source and Sink. The Authentication keys and tokens are established that helps in communication over Twitter Server.

Preprocessing 

• We extracted text from tweets and convert it to data frame, removed URLs from text, removed stop words like (the, a, to.), usernames and accounts, removed numbers and unnecessary spaces, removed punctuations.

Feature Extraction

• we extract the aspects from the processed dataset. Later this aspect is used to compute the positive and negative polarity in a sentence which is useful for determining the opinion

Feature Selection

• Having irrelevant features in your data can decrease the accuracy of the models and make your model learn based on irrelevant features.

Training and Classifier

• We uses a training set the subset to train a model. Supervised learning is an important technique for solving classification problems.

Benefits of the Project

The coaching staffs of sport team to understand well about players reviews, their issues related to game or others, their compliments before and after starting of the game. when the coach comes to know player's weaknesses then he will train the player in a better way to make him a successful player.

Technical Details of Final Deliverable

Supervised Machine Learning Classifers

Tools:Anaconda (spider)

Final Deliverable of the Project Software SystemCore Industry ITOther IndustriesCore Technology OthersOther TechnologiesSustainable Development Goals Good Health and Well-Being for PeopleRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	9500
GPU	Equipment	1	9500	9500