Description:
Field of the Invention

[0001] The present invention is related to the Computer Science field.

Background

[0002] The technology related to the general field of sentiment analysis on the internet platforms, specially the twitter micro-blog.
[0003] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0004] In the US Patent, US9519936B2, the Method and apparatus for analyzing and applying data related to customer interactions with social media is discussed. Basically, the invention was related to the use of social media. More particularly, the invention related to techniques for analyzing and applying data related to customer interactions with social media.
[0005] In another Patent, where the automatically constructing training sets for electronic sentiment analysis is shown. The objective is to use training data for training a neural network usable for electronic sentiment analysis can be automatically constructed. For example, an electronic communication usable for training the neural network and including multiple characters can be received. A sentiment dictionary including multiple expressions mapped to multiple sentiment values representing different sentiments can be received. Each expression in the sentiment dictionary can be mapped to a corresponding sentiment value. An overall sentiment for the electronic communication can be determined using the sentiment dictionary. Training data usable for training the neural network can be automatically constructed based on the overall sentiment of the electronic communication. The neural network can be trained using the training data. A second electronic communication including an unknown sentiment can be received. At least one sentiment associated with the second electronic communication can be determined using the neural network.
[0006] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0007] The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non- claimed element essential to the practice of the invention.
[0008] Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.

 
Objects of the Invention

[0009] The objective of developing an automated system is to do the sentiment analysis of twitter data posts.
[0010] Further the points of objectives are:
• To analyze a tweet a web page is set to take the input as of any twitter handle or any trend on twitter that data is then fetch from Twitter using Tweepy and Twitter restful API’s.
• The data fetched from the Twitter is then analyzed by a trained Machine Learning model to give the output as positive , neutral or negative based on the sentiment with which it is tweeted.

 
Summary of invention

[0011] A system and method is using the following technology:
● Python programming
● Flask Framework
● HTML and CSS Support
● Javascripting
● Modules such as NumPy, Pandas, Matplotlib, TextBlob, Tweepy

 
Drawings

Fig 1.

Drawing Brief Description:

[0012] Figure 1 shows the flow diagram of the present invention.

 
Advantages of the Invention

[0013] An advantage of the present invention is,
• To analyse the tweets on the twitter account
• To automated system, for the emotion analysis based on the posts.
• To make the system which is easy to implement and less manual efforts.
• Analysis of the twitter and create the dataset of the posts for researchers.
, Claims:We Claim:

1. A system and method is for the twitter sentiment analysis.
2. In system of claim 1, to develop machine learning model to predict the emotions and analyze the sentiments of the tweets done by the users.
3. In system of claim 1, it allows us to keep track of what's being said about our product or service on Twitter, and can help you detect angry customers or negative mentions before they escalate.
4. In system of claim 1, the invention is using the python, Flask, HTML, CSS and Java script.