Analizo
Analizo is a machine learning based solution that analyzes your reviews or comments on Facebook post of Quality Enhancement Cell (QEC) SBBU SBA and categorize them as positive or negative and then displays the summary of calculations. The users can see the summary to know about the overall sentiment
Project Title
Analizo
Project Area of Specialization
Project Summary
Analizo is a machine learning based solution that analyzes your reviews or comments on Facebook post of Quality Enhancement Cell (QEC) SBBU SBA and categorize them as positive or negative and then displays the summary of calculations. The users can see the summary to know about the overall sentiment of peoples about that post. It will also help the people to know about the reviews of the people perspectives.
Project Objectives
ML-based android application that performs sentiment analysis on Facebook post of Quality Enhancement Cell (QEC) SBBU SBA and also to know a user or audience opinion on a target object by analyzing a vast amount of reviews or comments from QEC post.
It is to Implement an algorithm for automatic classification of text into positive, negative, or neutral. Sentiment Analysis to determine the attitude of the mass is positive, negative or neutral towards the subject of interest.
Project Implementation Method
We are going to use ML to analyze the sentiments of poeple on facebook post of QEC by using java and python programming languages that will work with data sets to analyze sentiments for poeples or post creators and on the otherhand also we are going to use Tenser Flow, TenserFlow Lite, Android Studio, Firebase (Real-time Database), Libraries, Data collection and preparation.
Sentiment Analysis is defined as the computational study of opinions, sentiments, subjectivity, evaluations, attitudes, appraisal, affects, views, emotions, etc., expressed in post. Early work focused mainly on the overall positive or negative classification of a post. While detecting the overall sentiment of a post or snippet has a wide range of real-world applications, analyzing unstructured text of that post only in terms of positive and negative opinions -irrespectively of the entities mentioned in context and their aspects- is not sufficient enough to provide meaningful insights and is therefore of limited use.
Some review sites (e.g., Amazon, TripAdvisor) provide such information in the form of multiple-aspect user ratings. However, taking into account the textual component of user reviews provides also evidence to understand the reason behind the rating and results in better general or personalized review score predictions than those derived from the numerical star ratings given by the users.In addition, user ratings are not available in Social Media data like Twitter or Facebook. In this context, research has moved towards fine-grained approaches like aspect-based (or feature-based) sentiment analysis.
Benefits of the Project
- Android application that performs sentiment analysis on Facebook post of QEC.
- To improve the post or polices of QEC.
- To review quality standards of that post of QEC.
- To also help others universities.
- To promote public confidence that the quality and standards of the award of degree are enhanced and safeguarded.
- Analizo will help those poeple who will want system that analyze peoples sentiments to provide better understandings of them, so this app will help poeples and creators to avoid problems by using this app.
Technical Details of Final Deliverable
Sentiment Analysis is defined as the computational study of opinions, sentiments, subjectivity, evaluations, attitudes, appraisal, affects, views, emotions, etc., expressed in post. Early work focused mainly on the overall positive or negative classification of a post. While detecting the overall sentiment of a post or snippet has a wide range of real-world applications, analyzing unstructured text of that post only in terms of positive and negative opinions -irrespectively of the entities mentioned in context and their aspects- is not sufficient enough to provide meaningful insights and is therefore of limited use.
Some review sites (e.g., Amazon, TripAdvisor) provide such information in the form of multiple-aspect user ratings. However, taking into account the textual component of user reviews provides also evidence to understand the reason behind the rating and results in better general or personalized review score predictions than those derived from the numerical star ratings given by the users.In addition, user ratings are not available in Social Media data like Twitter or Facebook. In this context, research has moved towards fine-grained approaches like aspect-based (or feature-based) sentiment analysis.
Final Deliverable of the Project
Core Industry
Other Industries
Core Technology
Other Technologies
Sustainable Development Goals
Required Resources
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Domain | Equipment | 2 | 6000 | 12000 |
| Database | Equipment | 1 | 15000 | 15000 |
| ANACONDA Pro Version | Equipment | 6 | 3000 | 18000 |
| Stationary | Miscellaneous | 1 | 5000 | 5000 |
| Total in (Rs) | 50000 |
If you need this project, please contact me on contact@adikhanofficial.com
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