Derive sentiments from the text that has emojis and characters
Developed NLP and Ensemble Machine Learning-based Sentiment Analysis model for the Arabic language.
Successfully developed ensemble-based machine learning model for sentiment analysis. Six different sentiments, very positive, positive, very-negative, negative, neutral, and mixed kinds of sentiment predicated from social network posts represented in Arabic.
This entire solution is based on online social network post data.
After collecting the social network post data for text-wise sentiment analysis we did all the pre-processing steps like filtration, tokenization, stop-word removal, stemming, spell checking.
We used Stanford core NLP for POS-tagging and TF-IDF vectorization in feature extraction. Then we developed the model using Ensemble Machine Learning Approached.
For emoji-based sentiment analysis we classified all emojis into six different sentiments, very-positive, positive, very-negative, negative, neutral, and mixed, and saved in a dictionary. Then we extracted all emojis from text and found the Unicode and matched it with the dictionary to find the final sentiment.
After finding the sentiment from each module we applied the ensemble machine learning approach, found the weighted average, and based on the weighted average we found the final sentiment