Brand Sentiment Analysis using NLP

Brand Sentiment Analysis using NLP/Dataset/Dataset Card.csv

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+Dataset description,"Users assessed tweets related to various brands and products, providing evaluations on whether the sentiment conveyed was positive, negative, or neutral. Additionally, if the tweet conveyed any sentiment, contributors identified the specific brand or product targeted by that emotion."
+Columns,"tweet_text
+emotion_in_tweet_is_directed_at
+is_there_an_emotion_directed_at_a_brand_or_product"

Brand Sentiment Analysis using NLP/models/README.md

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+*Brand Sentiment Analysis using NLP*<br>
+*🎯 Goal*<br>
+The main goal of this project is to perform sentiment analysis on brand-related text data using Natural Language Processing (NLP) techniques. The purpose is to understand the sentiment expressed in textual content related to a brand, which can be valuable for businesses to gauge public opinion and improve their brand perception.<br>
+
+*🧵 Dataset*<br>
+The dataset used in this project is sourced from https://www.kaggle.com/datasets/tusharpaul2001/brand-sentiment-analysis-dataset . It comprises user-generated reviews and sentiments related to various brands. The dataset includes columns such as 'Brand', 'Category', 'Tweet', and 'Sentiment', providing a diverse set of textual data for sentiment analysis.<br>
+<br>
+
+*🧾 Description*<br>
+This project utilizes LSTM and BERT models for brand sentiment analysis. The LSTM model captures sequential patterns in the text, while the BERT model leverages contextual embeddings for more accurate sentiment analysis.<br>
+<br>
+
+*🧮 What I had done!*<br>
+1. Data Preprocessing:<br>
+
+Text cleaning and tokenization.<br>
+Handling missing or irrelevant data.<br>
+
+1. Feature Engineering:<bt>
+Word embeddings for LSTM.<br>
+Token embeddings for BERT.<br>
+
+3. Model Training:<br>
+Training the LSTM model on preprocessed text data.<br>
+Fine-tuning the BERT model on the specific sentiment analysis task.<br>
+
+4. Model Evaluation:<br>
+
+Evaluating models on test data.<br>
+Comparing the performance of LSTM and BERT models.<br>
+<br>
+
+*🚀 Models Implemented*<br>
+Long Short-Term Memory (LSTM) model<br>
+BERT (Bidirectional Encoder Representations from Transformers) model<br>
+
+Why these models:<br>
+
+LSTM: Suitable for capturing sequential patterns in text data.<br>
+BERT: Utilizes contextual embeddings, providing a deeper understanding of language context.<br>
+<br>
+
+
+*📚 Libraries Needed*<br>
+1. Tensorflow
+2. Keras
+3. Hugging Face Transformers
+4. Pandas
+5. Numpy
+6. Matplotlib
+7. Seaborn
+
+<br>
+
+*📊 Exploratory Data Analysis Results*<br>
+
+Sentiment Distribution<br>
+
+Insight: The distribution of sentiments across the dataset showcases the balance or imbalance between positive, negative, and neutral sentiments, guiding the model training process.<br>
+
+Brand-wise Sentiment Analysis<br>
+
+Insight: Analyzing sentiments specific to each brand reveals patterns and helps understand how different brands are perceived by users. This information is valuable for businesses aiming to enhance their brand image.<br>
+Feel free to replace the placeholder text with actual links to your visualizations or images and provide specific insights derived from your EDA. Including visuals helps make your README more engaging and informative.<br>
+<br>
+
+*📈 Performance of the Models based on Accuracy Scores*<br>
+1. LSTM Model Accuracy: 82.54%<br>
+2. BERT Model Accuracy: 81.18%<br>
+
+
+*📢 Conclusion*<br>
+The brand sentiment analysis project demonstrates the effectiveness of both LSTM and BERT models. While LSTM captures sequential nuances, BERT provides contextual understanding, resulting in improved sentiment analysis. The LSTM model achieved an accuracy of 82.54%, making it a valuable tool for brand perception analysis.<br>
+
+<br>
+
+✒️ Your Signature<br>
+Dipayan Majumder<br>
+https://github.com/dipayan22/<br>