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Sentimental Analysis for Twitter Data

Sentimental Analysis for Twitter Data

Price : 10000

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Course Duration
Approx 10

Course Price
₹ 10000

Course Level
High

Course Content

ABSTRACT

 

Analyzing sentiment is a process to identify the opinion of a text. It is also known as opinion mining or emotion Artificial Intelligence (AI). People post comments in social media mentioning their experience about an event and are also interested to know if the majority of other people had a positive or negative experience on the same event. This classification can be achieved using Sentiment Analysis. Sentiment analysis takes unstructured text comments about a product reviews, an event, etc., from all comments posted by different users and classifies the comments into different categories as either positive or negative or neutral opinion. This is also known as polarity classification. Sentimental analysis can be performed by Text analysis and computational linguistics. This work aims at comparing the performance of different machine learning algorithms in performing sentiment analysis of Twitter data

 

1.1           INTRODUCTION

Analyzing sentiment is a process to find out the opinion of a text. People post comments in social media mentioning their experience about an event and are also interested to know if the majority of other people had a positive or negative experience on the same event. Analyzing sentiment is a process of knowing users emotions for a particular item which may be an event or topic or individual of recent trends. Sentiment analysis can be done at three levels and they are sentence, aspect and document level. Twitter is a highly rich source of information for deciding the quality of any product. Twitter platform uses tweets which are in sentence form to denote opinions. Therefore, sentiment analysis at sentence level is used for examining sentiments. Sentiment analysis over Twitter provides the companies an effective way to find the opinions of people towards their newly launched products. The goal is to calculate the sentiment accuracy of sentences that were extracted from the text of tweets. Sentiment analysis can be done for twitter data which will classify the tweet as either positive or negative. This analysis helps concerned organizations to find opinions of people about their product, events, so on from the tweets. The opinion words are the most challenging part in sentiment analysis. An opinion word may be positive or negative depending on the situation. The meaning of the content will not be altered by traditional text processing systems when there is a little change in the words. But sentiment analysis can change the meaning of the content when there are changes in two words. For example, “The phone is ringing” is different from “the phone is not ringing”. The processing is done sentence by sentence. The informal sentence in twitter can be understood by the user whereas the system cannot understand. We proposed a system to do sentimental analysis for twitter data using machine learning techniques and algorithms.

 

BLOCK DIAGRAM

BLOCK DIAGRAM

SYSTEM REQUIREMENTS

Hardware Requirement:-

      System: Pentium IV 2.4 GHz. 

      Hard Disk: 500 GB.

      Ram: 4 GB.

      Any desktop / Laptop system with above configuration or higher level.

 

Software Requirements:-

      Operating system : Windows XP / 7

      Coding Language :Python

      Interpreter    :Pyhton 3.6

      IDE             : Jupyter notebook

      ML APIS           :.tkinter ,Numpy, Opencv, tensorflow ,Pandas

 

Functional Requirements:-

      Prediction of product review analysis.

      A functional classifier for accurate and automatic sentiment classification.

 

Non Functional Requirements:-

They basically deal with issues like:

      Security

      Maintainability

      Reliability

      Scalability

      Performance

CONCLUSION

 

Sentiment analysis is a process to identify the opinion of a text. People post comments in social media mentioning their experience about an event and are also interested to know if the majority of other people had a positive or negative experience on the same event. The goal is to calculate the sentiment accuracy of sentences that were extracted from the text of tweets. The sentiment analysis of the tweet helps to find whether the sentiment of the tweet on particular products, events, etc., is positive or negative. Sentiment analysis is developed to investigate public views towards a tweet/hash tag. Sentiment analysis can be exploiting for business process decision.  We are going to develop a functional classifier for accurate and automatic sentiment classification of an unknown tweet stream.

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