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nocturne2333's Projects

aobtm-adaptive-online-biterm-topic-modeling

Input: <version-sliced-reviews>. To track the topic variations over versions, a novel method AOBTM is employed for generating version-sensitive topic distributions. The emerging topics are then identified based on the typical anomaly detection method.

aspect_sentiment_analysis

An investigation into sentiment analysis and topic modelling techniques.

ast-text-analysis

Statistical Natural Language Processing with Annotated Suffix Trees

avatar-sentiment-analysis

beautiful soup web scraping -> nltk & vader sentiment analysis -> nrc emotional lexicon classification -> chartJS graph generation. Check out the results! =>

awesome-sentiment-analysis

Repository with all what is necessary for sentiment analysis and related areas

bert-topic-sentiment

Topic Based Sentiment Detection using BERT

bmm

A Dirichlet Process Biterm-based Mixture Model for Short Text Stream Clustering

chinesenlpcorpus

An collection of Chinese nlp corpus including basic Chinese syntatic wordset, semantic wordset, historic corpus and evaluate corpus. 中文自然语言处理的语料集合，包括语义词、领域共时、历时语料库、评测语料库等。

cnn-news-classification-tf

Applying dennybritz version of Kim Yoons CNN-classification to entity level sentiment analysis of multilabel newsdata

coi

练手项目：Comment of Interest 电商文本评论数据挖掘（爬虫 + 观点抽取 + 句子级和观点级情感分析）

comparison-of-hybrid-neural-network-methodologies-for-sentiment-emotion-analysis

Twitter tweets play an important role in every organisation. This project is based on analysing the English tweets and categorizing the tweets based on the sentiment and emotions of the user. The literature survey conducted showed promising results of using hybrid methodologies for sentiment and emotion analysis. Four different hybrid methodologies have been used for analysing the tweets belonging to various categories. A combination of classification and regression approaches using different deep learning models such as Bidirectional LSTM, LSTM and Convolutional neural network (CNN) are implemented to perform sentiment and behaviour analysis of the tweets. A novel approach of combining Vader and NRC lexicon is used to generate the sentiment and emotion polarity and categories. The evaluation metrics such as accuracy, mean absolute error and mean square error are used to test the performance of the model. The business use cases for the models applied here can be to understand the opinion of customers towards their business to improve their service. Contradictory to the suggestions of Google’s S/W ratio method, LSTM models performed better than using CNN models for categorical as well as regression problems.

dynamic-nmf

Dynamic Topic Modeling via Non-negative Matrix Factorization

ekmanssentimentanalysis

Lexicon Based Sentiment Analysis for Ekman's 6 Emotions

ernie

An Implementation of ERNIE For Language Understanding (including Pre-training models and Fine-tuning tools)

fasttext-for-ai-challenger-sentiment-analysis

AI Challenger 2018 Sentiment Analysis Baseline with fastText

finegrainedopinionmining

细粒度情感分析repository2：细粒度情感分析接口，aspect-based sentiment analysis based on HMM.

gdeltdatascripts

Scripts to process GDELT data and perform sentiment analysis based on events and emotions in global news data

google_news_scraper_and_sentiment_analyzer

Downloads news articles from Google news and uses pre-trained NLP models to perform sentiment analysis

jointtsmodel

Library of Joint Topic-Sentiment Models

lda

Topic modeling with latent Dirichlet allocation using Gibbs sampling

leap-t

LEAP-T is a lexicon-based approach to emotion analysis of text in tweets, using a specified hashtag over a selected date range. It uses the NRC's Emotion Lexicon.