This project aims to classify the reviews on medicines and treatment recieved by patients as positive, negative and neutral. This is a solution for a hackathon organized on Analytics Vidhya.
Sentiment Analysis for drugs/medicines Nowadays the narrative of a brand is not only built and controlled by the company that owns the brand. For this reason, companies are constantly looking out across Blogs, Forums, and other social media platforms, etc for checking the sentiment for their various products and also competitor products to learn how their brand resonates in the market. This kind of analysis helps them as part of their post-launch market research. This is relevant for a lot of industries including pharma and their drugs.
The challenge is that the language used in this type of content is not strictly grammatically correct. Some use sarcasm. Others cover several topics with different sentiments in one post. Other users post comments and reply and thereby indicating his/her sentiment around the topic.
Sentiment can be clubbed into 3 major buckets - Positive, Negative and Neutral Sentiments.
You are provided with data containing samples of text. This text can contain one or more drug mentions. Each row contains a unique combination of the text and the drug mention. Note that the same text can also have different sentiment for a different drug.
The following files were provided as a dataset:
- train_F3WbcTw.csv: Train dataset
- test_tOlRoBf.csv: Test dataset
The datasets have the following texts: unique_hash: Unique ID text: text pertaining to the drugs drug: drug name for which the sentiment is provided sentiment: (Target) 0-positive, 1-negative, 2-neutral (only in training dataset)
Code Files:
- sentiment_analysis.py: Code containing XGBoost and SVM classifiers trained with TF-IDF features.
The metric used for evaluating the performance of the classification model is macro F1-Score.