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Concept Level Sentiment Analysis of Bengali Text

Overview

This project explores the concept-level sentiment analysis of Bengali text, focusing on restaurant reviews, comments, and blogs. Unlike traditional feature-based sentiment analysis, this research introduces a concept-level approach using custom-built dictionaries to identify and classify sentiments more accurately.

Concept Level Sentiment Analysis

Key Features

  • Data Collection:

    • Collected 2,700 Bengali text reviews, comments, and blogs from Facebook.
    • Labeled data manually into positive, negative, and neutral sentiment categories.

  • Preprocessing Workflow:

    • Punctuation Removal: Removed unnecessary symbols and spaces.
    • Tokenization & Detokenization: Processed text using nltk for splitting and rejoining meaningful words.
    • Stopword Removal: Eliminated uninformative words like "অথচ," "অথবা," and "এটি."
    • Concept Construction: Built custom dictionaries for nouns, adjectives, verbs, and adverbs to extract sentiment-bearing concepts.

  • Machine Learning Models:

    • Evaluated algorithms: Naïve Bayes, Support Vector Machine (SVM), Random Forest, Decision Tree, and K-Nearest Neighbor.
    • Compared model performance on feature-based and concept-level datasets.

Methodology

  1. Concept Extraction:

    • Created self-made dictionaries to identify sentiment-rich concepts.
    • Mapped these concepts to BanglaSenticNet for sentiment matching.

  2. Data Processing:

    • Cleaned data and transformed text into concepts using custom algorithms.
    • Generated two datasets: one for feature-based analysis and another for concept-level analysis.

  3. Evaluation Metrics:

    • Used metrics such as accuracy, precision, recall, and F1-score for model performance evaluation.
    • Confusion matrix to analyze predictions.

Results

  1. Feature-Based Approach:

    • SVM achieved the highest accuracy: 84%.
    • Other models' accuracy: Random Forest (73%), Decision Tree (72%), Naïve Bayes (66%).

  2. Concept-Level Approach:

    • SVM achieved the highest accuracy: 96%.
    • Other models' accuracy: Random Forest (75%), Decision Tree (71%), Naïve Bayes (68%), K-Nearest Neighbor (68%).

  3. Performance Comparison:

    • The concept-level approach outperformed feature-based models across all metrics, showcasing its efficacy in sentiment analysis for Bengali text.

Conclusion

  • The proposed concept-level approach improves sentiment detection by incorporating domain-specific knowledge from dictionaries.
  • SVM consistently outperformed other models, validating its robustness in both feature-based and concept-level approaches.
  • The research highlights the challenges of extracting meaningful concepts from Bengali text and the effectiveness of custom-built dictionaries.

Future Directions

  1. Advanced Concept Extraction:
    • Implement dependency-based semantic parsing for improved results.
  2. Expanding Sentiment Labels:
    • Move beyond positive, negative, and neutral to include emotions like joy, anger, and sadness.
  3. Larger Datasets:
    • Work with a more extensive and diverse dataset to generalize findings.
  4. Multi-language Support:
    • Apply the methodology to other languages using R or Python.

References

Key references include:

  • BanglaSenticNet for concept matching.
  • SentiWordNet for sentiment classification.
  • Various research papers on multilingual sentiment analysis and dependency parsing.

For more details, please refer to the research paper attached in this repository.