Telugu Sentiment Analysis

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Telugu Sentiment Analysis

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Overview
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Detail Description

1. Abstract

Telugu Sentiment Analysis is an AI-based application that analyzes Telugu text and identifies the emotional polarity expressed in it. Using Natural Language Processing (NLP) and machine learning techniques, the system classifies text such as social media posts, product reviews, and user comments into positive, negative, or neutral categories. This project helps organizations understand public opinions, analyze customer feedback, and gain insights from Telugu-language digital content.

2. Objectives

To analyze sentiment in Telugu text automatically.
To classify text into positive, negative, and neutral sentiments.
To process Telugu social media posts, reviews, and comments.
To understand customer opinions and public reactions.
To reduce the manual effort involved in sentiment analysis.
To support opinion mining for Telugu-language content.

3. Existing System

Traditional sentiment analysis often focuses on English text or relies on manually created dictionaries and basic classification techniques. Telugu text presents additional challenges because of its language structure, spelling variations, informal expressions, and code-mixed Telugu-English content.

Disadvantages

Limited support for Telugu-language content in some tools.
Difficulty understanding context and sarcasm.
Spelling variations can affect classification accuracy.
Code-mixed text can be difficult to process.
Manual analysis of large datasets is time-consuming.

4. Proposed System

The proposed system uses Telugu language-processing techniques and machine learning or deep learning models to identify sentiment from user-provided text. The input is cleaned and processed before a trained model predicts whether the sentiment is positive, negative, or neutral. The application displays the predicted category and can provide sentiment statistics for collections of reviews or social media comments.

Key Features

Telugu text input and sentiment prediction.
Positive, negative, and neutral classification.
Text preprocessing and normalization.
Support for analyzing multiple text records.
Visual summaries of sentiment distribution.
Potential support for Telugu-English code-mixed text.

5. Implementation Procedure

Collect a labeled Telugu text dataset containing sentiment categories.
Clean the text and normalize Telugu characters and common spelling variations.
Tokenize and preprocess the text using suitable NLP techniques.
Convert the text into numerical features using TF-IDF, word embeddings, or transformer representations.
Train a classification model such as Logistic Regression, Support Vector Machine, or a suitable transformer model.
Evaluate the model using accuracy, precision, recall, and F1-score.
Develop a user interface using Streamlit or a web framework.
Allow users to enter Telugu text or upload a dataset.
Display sentiment predictions and summary visualizations.
Test the system with different writing styles and code-mixed inputs.

6. Software Requirements

Programming Language: Python
Frontend: Streamlit or HTML, CSS, and JavaScript
NLP Libraries: Indic NLP Library, NLTK, or Transformers
Machine Learning: Scikit-learn
Deep Learning: PyTorch or TensorFlow
Data Processing: Pandas and NumPy
Visualization: Matplotlib or Plotly
Development Environment: VS Code or Jupyter Notebook
Operating System: Windows, Linux, or macOS

7. Hardware Requirements

Processor: Intel Core i3 or equivalent and above
RAM: Minimum 4 GB; 8 GB recommended
Storage: At least 5 GB of free space
GPU: Optional for traditional ML models; recommended for training larger transformer models
Internet Connection: Required for downloading datasets and pretrained models

8. Advantages of the Project

Automatically analyzes Telugu-language opinions.
Helps understand customer feedback and public sentiment.
Processes large volumes of text quickly.
Supports Telugu-language social media analysis.
Reduces manual classification effort.
Can be extended to multilingual and code-mixed sentiment analysis.
Provides useful insights through sentiment reports and visualizations.

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