Machine_learning_on_Amazon_review

This python project explains how to implement sentiment analysis using machine learning (SVM) on amazon Alexa reviews dataset. The dataset used for the project is obtained from Kaggle and consists of nearly 3000 reviews from amazon users regarding various amazon Alexa products like Alexa echo, Alexa dot etc.

1. Introduction and Dataset Source:

   • Explain the purpose of the project: sentiment analysis on Amazon Alexa reviews.
   • Mention that the dataset is obtained from Kaggle and includes around 3000 reviews.

2. Exploratory Data Analysis (EDA):

   • Describe the process of exploring the dataset.
   • Highlight the columns/features available in the dataset.
   • Explain the importance of understanding the data before proceeding.

3. Data Visualization:

   • Mention that you used count plots and pie charts.
   • Describe what insights you gained from these visualizations, such as distribution of sentiment classes.

4. Data Preprocessing:

   • Explain the various preprocessing steps you applied to the reviews:
     • Lowercasing the text.
     • Removing URLs and punctuation.
     • Tokenization (splitting text into words/tokens).
     • Removing stop words.
     • Applying stemming (reducing words to their root form).

5. Data Labeling:

   • Describe how you separated the processed data into positive and negative sentiment categories.
   • Highlight the importance of labeled data for training the machine learning model.

6. Word Cloud Visualization:

   • Explain the concept of word clouds.
   • Mention that you used word clouds to visualize the most frequent words in positive and negative reviews.
   • Discuss any interesting insights you gained from these word clouds.

7. Machine Learning Model - Support Vector Machine (SVM):

   • Introduce the SVM classifier as the chosen machine learning algorithm.
   • Mention that SVM is used for its ability to handle high-dimensional data and binary classification tasks effectively.

8. Model Training and Evaluation:

   • Describe how you split the dataset into training and testing sets.
   • Explain how you trained the SVM model on the preprocessed data.
   • Discuss the metrics used to evaluate the model's performance (accuracy, precision, recall, F1-score, etc.).

9. Conclusion:

   • Summarize the key findings and outcomes of the project.
   • Reflect on the effectiveness of using SVM for sentiment analysis on Amazon Alexa reviews.
   • Discuss any limitations or areas for improvement in the project.

10. References:

    • Mention any external sources, libraries, or packages you used for the project.