Pinned Repositories
AnkitaSharma-rgb
Config files for my GitHub profile.
Blog_References
Code and content references for TechnovativeThinker blog posts
multi-label-classification
A multi label classification for identifying the most probabilistic companies a problem might be asked upon in its interview. It includes several approaches like label transformation, algorithm adaption, ensemble learning and LSTM. Base classifiers like Gaussian NB, Multinomial NB, Logistic Regression, Descision Tree, Random Forest and SVC is used.
NormalizedNerd
Codes for the videos of my YouTube channel
ROUGE-evalution-metrics-in-Colab
pyrouge installation for Google Colab.
Sentiment-Analysis-On-Hindi-Reviews
We have used 250 sentences of movie reviews available for research from IIT bombay and also crawled and manually annotated 750 reviews from jagran.com, In total 1000 reviews. After preprocessing the dataset, We generate the featureset as a vector-based approach using Term frequency, tfidf for unigrams and bigrams. Then we used three approaches to predict the sentiment of a review. Approaches used are Resource based, In-language semantic analysis and Machine Translation based semantic analysis.
Sentiment_analysis_movie_review
This program analyses positive and negative reviews of Movie Review data obtained from http://www.cs.cornell.edu/people/pabo/movie-review-data and also predicts new reviews as postive or negative.
Text_Classification
Text Classification Algorithms: A Survey
Twittter-sentiment-analysis
classification of tweets as positive or negative
AnkitaSharma-rgb's Repositories
AnkitaSharma-rgb/AnkitaSharma-rgb
Config files for my GitHub profile.
AnkitaSharma-rgb/Blog_References
Code and content references for TechnovativeThinker blog posts
AnkitaSharma-rgb/multi-label-classification
A multi label classification for identifying the most probabilistic companies a problem might be asked upon in its interview. It includes several approaches like label transformation, algorithm adaption, ensemble learning and LSTM. Base classifiers like Gaussian NB, Multinomial NB, Logistic Regression, Descision Tree, Random Forest and SVC is used.
AnkitaSharma-rgb/NormalizedNerd
Codes for the videos of my YouTube channel
AnkitaSharma-rgb/ROUGE-evalution-metrics-in-Colab
pyrouge installation for Google Colab.
AnkitaSharma-rgb/Sentiment-Analysis-On-Hindi-Reviews
We have used 250 sentences of movie reviews available for research from IIT bombay and also crawled and manually annotated 750 reviews from jagran.com, In total 1000 reviews. After preprocessing the dataset, We generate the featureset as a vector-based approach using Term frequency, tfidf for unigrams and bigrams. Then we used three approaches to predict the sentiment of a review. Approaches used are Resource based, In-language semantic analysis and Machine Translation based semantic analysis.
AnkitaSharma-rgb/Sentiment_analysis_movie_review
This program analyses positive and negative reviews of Movie Review data obtained from http://www.cs.cornell.edu/people/pabo/movie-review-data and also predicts new reviews as postive or negative.
AnkitaSharma-rgb/Text_Classification
Text Classification Algorithms: A Survey
AnkitaSharma-rgb/Twittter-sentiment-analysis
classification of tweets as positive or negative