555 Open source projects that are alternatives of or similar to text_analysis_tools

Sentiment analysis on Amazon Review Dataset available at http://snap.stanford.edu/data/web-Amazon.html

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AFINN-based sentiment analysis for Node.js.

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A comparison and discussion of different NLP methods for 5-class sentiment classification on the SST-5 dataset.

TextClf ：基于Pytorch/Sklearn的文本分类框架，包括逻辑回归、SVM、TextCNN、TextRNN、TextRCNN、DRNN、DPCNN、Bert等多种模型，通过简单配置即可完成数据处理、模型训练、测试等过程。

Sentiment Analysis LSTM recurrent neural network's.

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The aim of this repository is to show a baseline model for text classification by implementing a LSTM-based model coded in PyTorch. In order to provide a better understanding of the model, it will be used a Tweets dataset provided by Kaggle.

Implementation of papers for text classification task on SST-1/SST-2

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Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall

This repository contains code and bonus content which will be added from time to time for the book "Learning Social Media Analytics with R" by Packt

中文自然语言处理工具集【断句/分词/词性标注/组块/句法分析/语义分析/NER/N元语法/HMM/代词消解/情感分析/拼写检查】

Aspect Based Sentiment Analysis

No description or website provided.

Sentiment analysis library for russian language

This script can tell you the sentiments of people regarding to any events happening in the world by analyzing tweets related to that event

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The code uses the tweepy library to access the Twitter API and the TextBlob library to perform Sentiment Analysis on each Tweet.

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This repository contains PyTorch implementation for the baseline models from the paper Utterance-level Dialogue Understanding: An Empirical Study

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