Scribd
Upload
23 views8 pages

Machine Learning Sentiment Analysis

The document outlines a project aimed at developing a machine learning model for automated sentiment analysis of textual data to aid decision-making. It details the objectives, methodology, timeline, resources needed, and expected outcomes of the project. The model will classify sentiments as positive, negative, or neutral and provide insights for various applications such as product reviews and social media analysis.

Uploaded by

atharvabhagat935
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as ZIP, PDF, TXT or read online on Scribd
23 views8 pages

Machine Learning Sentiment Analysis

The document outlines a project aimed at developing a machine learning model for automated sentiment analysis of textual data to aid decision-making. It details the objectives, methodology, timeline, resources needed, and expected outcomes of the project. The model will classify sentiments as positive, negative, or neutral and provide insights for various applications such as product reviews and social media analysis.

Uploaded by

atharvabhagat935
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as ZIP, PDF, TXT or read online on Scribd
You are on page 1/ 8

Sentiment Analysis Using

Machine Learning
Team Members:
1. Atharva Bhagat
2. Om Devharkar
3. Sahil korgoankar
4. Anushka Waghmare
Problem Statement

Understanding public sentiment on various topics (e.g., products, services,


social issues) is crucial for decision-making. Traditional methods are time-
consuming and subjective. Automating sentiment analysis with machine
learning can provide faster, more accurate insights.

This project aims to develop a machine learning model that can accurately
classify and analyze sentiment in textual data, facilitating better decision-
making for businesses.
Objectives

• Automate the sentiment analysis process using machine learning


algorithms.
• Classify text data into positive, negative, or neutral sentiments.
• Improve the accuracy and efficiency of sentiment prediction.
• Apply the model to various real-world applications like product reviews,
social media, etc.
Proposed Methodology

1. Data Collection: Gather text data from sources like social media,
reviews, or datasets (e.g., IMDb, Twitter).
2. Data Preprocessing: Clean and preprocess the data (remove noise,
stopwords, tokenize).
3. Model Selection: Choose appropriate ML algorithms (e.g., Naive Bayes,
SVM, or neural networks).
4. Training: Train the model using labeled datasets.
5. Testing and Evaluation: Validate the model’s accuracy using metrics
like precision, recall, and F1-score.
Timeline and Milestones

• Week 1: Data collection and preprocessing.


• Week 2: Model selection and initial training.
• Week 3: Model testing and evaluation.
• Week 4: Final tuning, application, and presentation.
Resources Needed

• Datasets: Publicly available sentiment datasets (e.g., IMDb, Twitter).


• Software/Tools: Python, scikit-learn, NLTK, TensorFlow/PyTorch.
• Hardware: Personal computers or cloud computing resources (if needed
for large datasets).
Expected Outcome
• A trained sentiment analysis model capable of classifying text into
positive, negative, or neutral sentiments.
• Insights from text data that can assist in decision-making for
businesses, media monitoring, and customer feedback analysis.
Expected Outcome
Thank
• A trained sentiment analysis model capable of classifying text into
positive, negative,You
or neutral sentiments.
• Insights from text data that can assist in decision-making for
businesses, media monitoring, and customer feedback analysis.
Any
Questions?

You might also like