CommentAnalyzer is a machine learning project focused on analyzing Instagram comments to extract valuable insights through sentiment analysis, question detection, and topic modeling. By leveraging advanced algorithms such as LDA, LSA, NMF, and BERT, this project aims to provide a comprehensive understanding of user interactions on social media platforms.
The main objectives of CommentAnalyzer are:
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Sentiment Analysis: Develop models to analyze user sentiments in comments to identify positive, negative, and neutral feelings. This analysis can help brands and researchers gain a better understanding of user opinions and sentiments towards their products or services.
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Question Detection: Implement algorithms to identify and categorize comments that are posed as questions. This capability includes detecting questions based on specific question words such as "how," "what," "where," etc., which can enhance user interaction and facilitate quicker responses to their needs.
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Topic Modeling: Utilize topic modeling techniques such as LDA, LSA, and NMF to identify and extract the main topics discussed in comments. This information can assist in analyzing trends and user interests.
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Word Cloud Creation: Generate word clouds to visually represent the frequency of words and main topics in comments, helping users quickly grasp the core content of the feedback.
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Sentiment Analysis Visualization: Analyze and visualize the sentiments expressed in comments to provide insights into user emotions and opinions.
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Text Cleaning and Emoji Processing: Effectively preprocess text data by removing unwanted characters, URLs, and stopwords, while also properly processing emojis to accurately represent sentiments.
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Collection of Persian Stopwords: Create a comprehensive list of Persian stopwords from various sources to improve text analysis quality by filtering out non-meaningful words.
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Word Frequency Analysis and Control Character Removal: Analyze word frequencies and ensure clean data by removing control characters that may disrupt text processing.
CommentAnalyzer is designed for:
- Social Media Analysts: Professionals seeking insights into user sentiments and trends on Instagram.
- Marketers: Individuals looking to better understand audience interactions and improve their marketing strategies based on user feedback.
- Researchers: Academics and researchers interested in sentiment analysis and natural language processing (NLP) in the context of social media.
- Developers: Software developers and data scientists who want to implement sentiment analysis and question detection in their applications, such as chatbots or customer support systems.
CommentAnalyzer can be utilized in various applications, including:
- Enhancing customer support systems by identifying user inquiries.
- Informing marketing strategies through sentiment analysis of user comments.
- Conducting research on user behavior and sentiment trends on social media.
You can easily run the CommentAnalyzer project in Google Colab. Click the button below to open the notebook:
By providing tools for sentiment analysis, question detection, and effective text preprocessing, CommentAnalyzer aims to empower users with the insights needed to foster meaningful connections and improve engagement on social media.
You can access the dataset used for this project at the following link: Persian Comment Dataset on Instagram.