#Senti Star
OBJECTIVE
We Propose to built a gateway of sentiment analysis that capable of process almost every file type whether image,audios, pdf,ppt,doc,Youtube comments, mails, Blogs, webssites,videos . etc , Our Webapp can come to conclusion for even large amount of data with sentiment report within seconds.
The Objective of the Project is to solve problems in the space of Natural Language Processing with help of automation and Machine Learning.
This Project will solve Problem of going through each reviews and getting the emotion of the text.
Deliverables include Statistical Charts and Scores for Positivity and Negativity of Text.
PROJECT DESCRIPTION
The Application consists of 6 Microservices integrated to a Main Data Aggregator
The Flow is as Follows, User is Initially Authenticated and the Landed on The Data Aggregator application. Where in They get 5 Different Features for sentiment classification.
User Authentication : The Initial Phase of the Project ,This Module Deals
With User Login,Registration via integrations Like Google and Github
Data Aggregator : This Module is the Core application aggregates all different Microservices. Built With Django and Maintains all the transitions to Different Microservices via DataBase
Sent-Online : This Microservice deals with Detecting human emotions by capturing Text online. Has a Separate Page to Take Input and Process it further.
Senti-Mage :This Microservice deals with Detecting human emotions by capturing Images online.This Module uses OCR (Optical Character Recognition) for sentiment recognition and EmoRec API for Classification
Sent-File : This Microservice deals Rendering a Statistical Dashboard for Input File. This Module supports different files like txt,pdf,csv,doc,docx . This also comes with the option to download the Charts.
Senti-Trans : This Module Deals with Translating given text and then analyses sentiment of text using EmoRe API.
Senti-Speech : This Microservice deals with Detecting human emotions by capturing Speech online. Has a Page to upload that speech and Detect the Sentiment.
Senti-Url: This Microservice handles the articles, Blogs,Urls and mail
,Its able scrape and parse the data from websites and gives Sentiment
EXPERIMENTAL RESULTS
Accuracy : 1274 / 1500
=
85%
Recall : 802 / ( 802 + 118)
=
87%
Precision : 802 / (802 + 108)
=
88%
FUTURE WORK
The future work on this will include Integrating this project with several other domains like Identifying and Predicting Market Trends, Keeping an eye on the brand’s image, Examining public opinion polls and political polls, Data from customer feedback is being analyzed, Observing and analyzing conversations on social media, Employee Turnover Reduction and Many More.
Real-time API Deploy the Whole Application Integrate with GPT-4 for better accuracy
REFERENCES
[1].Feldman, Ronen. "Techniques and applications for sentiment analysis." Communications of the ACM 56, no. 4 (2013): 82-89.
[2]. Kiritchenko, Svetlana, Xiaodan Zhu, and Saif M. Mohammad. "Sentiment analysis of short informal texts." Journal of Artificial Intelligence Research 50 (2014): 723-762.
[3]. Baek, Y., Lee, B., Han, D., Yun, S., & Lee, H. (2019). Character Region Awareness for Text Detection. ArXiv. https://doi.org/10.48550/arXiv.1904.01941
[4]. Kirange, D. K., and R. R. Deshmukh. "Emotion classification of news headlines using SVM." Asian Journal of Computer Science and Information Technology 5, no. 2 (2012): 104-106.
[5].Alotaibi, Fahad Mazaed. "Classifying text-based emotions using logistic regression." (2019).
[6].harupa, Nazia Anjum, et al. "Emotion detection of the Twitter post using multinomial Naive Bayes." 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 2020. [7].Le, Anh Duc, Dung Van Pham, and Tuan Anh Nguyen. "Deep learning approach for receipt recognition." Future Data and Security Engineering: 6th International Conference, FDSE 2019, Nha Trang City, Vietnam, November 27–29, 2019, Proceedings 6. Springer International Publishing, 2019.
[8].Zhang, Zixing, et al. "Deep learning for environmentally robust speech recognition: An overview of recent developments." ACM Transactions on Intelligent Systems and Technology (TIST) 9.5 (2018): 1-28.
[9].Harár, Pavol, Radim Burget, and Malay Kishore Dutta. "Speech emotion recognition with deep learning." 2017 4th International conference on signal processing and integrated networks (SPIN).
[10].Chong, Wei Yen, Bhawani Selvaretnam, and Lay-Ki Soon. "Natural language processing for sentiment analysis: an exploratory analysis on tweets." 2014 4th international conference on artificial intelligence with applications in engineering and technology.
[11].Narayanan, Vivek, Ishan Arora, and Arjun Bhatia. "Fast and accurate sentiment classification using an enhanced Naive Bayes model." Intelligent Data Engineering and Automated Learning–IDEAL 2013: 14th International Conference, IDEAL 2013, Hefei, China, October 20-23, 2013. Proceedings 14. Springer Berlin Heidelber