https://github.com/venkat-0706/sentimental-analysis

Build a model to classify text as positive, negative, or neutral. Apply NLP techniques for preprocessing and machine learning for classification. Aim for accurate sentiment prediction on various text formats.
https://github.com/venkat-0706/sentimental-analysis

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Build a model to classify text as positive, negative, or neutral. Apply NLP techniques for preprocessing and machine learning for classification. Aim for accurate sentiment prediction on various text formats.

• Host: GitHub
• URL: https://github.com/venkat-0706/sentimental-analysis
• Owner: venkat-0706
• License: mit
• Created: 2024-08-16T07:07:07.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-08-16T07:51:35.000Z (5 months ago)
• Last Synced: 2024-08-16T08:43:39.132Z (5 months ago)
• Language: Jupyter Notebook
• Size: 280 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Sentimental-Analysis

Absolutely! Here's a breakdown of the Coursera Project Network project focusing on sentiment analysis, similar to the previous breakdown but tailored to this specific task:

**Project Scenario:**

* The goal is to understand user sentiment towards a brand or product based on social media posts.

* You'll use Python to collect social media data (e.g., tweets), analyze the text content, and classify sentiment (positive, negative, neutral).

* This analysis will help the agency tailor their marketing strategies to better resonate with the target audience.

**Project Objectives:**

* Gain insights into user sentiment towards a brand or product.

* Inform marketing strategies based on user sentiment analysis.

* Improve brand perception and increase customer engagement.

** Challenge:**

* Collect social media data relevant to a chosen brand or product.

* Preprocess the text data (cleaning, normalization).

* Build a sentiment analysis model using machine learning techniques.

* Evaluate the model's performance and refine it if necessary.

* Analyze the results and draw meaningful conclusions about user sentiment.

**Project Deliverables:**

* A sentiment analysis model capable of classifying user opinions (positive, negative, neutral)

* A report summarizing the findings and insights about user sentiment.

* Recommendations for the marketing agency based on the sentiment analysis.

**Key Skills Developed:**

* Text Preprocessing Techniques (lowercasing, tokenization, stop word removal)

* Machine Learning Algorithms for Classification (e.g., Naive Bayes, Logistic Regression)

* Data Visualization Techniques for Presenting Findings

**Project Breakdown:**

1. **Data Acquisition:**

    * Choose a social media platform (e.g., Twitter) and relevant API for data collection.

    * Focus on data related to the brand or product of interest.

    * Consider using libraries like Tweepy (for Twitter) to collect data.

2. **Data Preprocessing:**

    * Clean the text data by removing irrelevant information (e.g., URLs, punctuation).

    * Normalize the text by converting to lowercase, handling emojis, and stemming/lemmatization.

    * Tokenize the text into individual words for further analysis.

3. **Sentiment Analysis Model Building:**

    * Choose a suitable machine learning algorithm for sentiment classification.

    * Train the model on a labeled dataset where sentiment is already identified (positive, negative, neutral).

    * Evaluate the model's performance using metrics like accuracy, precision, and recall.

    * Fine-tune the model if necessary to improve classification accuracy.

4. **Analysis and Recommendations:**

    * Analyze the sentiment distribution (positive vs. negative vs. neutral) towards the brand or product.

    * Identify common themes or topics associated with positive and negative sentiment.

    * Generate insights about user perception and areas for improvement.

    * Recommend marketing strategies based on the sentiment analysis findings.

**Additional Considerations:**

* Explore techniques like handling sarcasm or negation to enhance model accuracy.

* Consider visualizing the sentiment distribution using charts or word clouds.

* Address limitations of the project, such as data bias or chosen algorithms.