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mihai surdeanu's Semantic Role Labeler. Updated headers to be gcc > 4.3 compatible. - SwiRL/model_swirl/lemma.counts at master · kvh/SwiRL

SwiRL/model_swirl/lemma.counts at master · kvh/SwiRL · GitHub

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Bayesian inference for learning Tree Substitution Grammars - dptsg/data/lex.02-21 at master · mjpost/dptsg

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C++ interface between SISTA's CoreNLP processors and SwiRL. - SwiRL-NLP/model_swirl/lemma.counts at master · trananh/SwiRL-NLP

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This project addresses the problem of sentiment analysis on Twitter. The goal of this project was to predict sentiment for the given Twitter post using Python. Sentiment analysis can predict many different emotions attached to the text, but in this report, only 3 major were considered: positive, negative and neutral. The training dataset was small (just over 5900 examples) and the data within it was highly skewed, which greatly impacted on the difficulty of building a good classifier. After creating a lot of custom features, utilizing bag-of-words representations and applying the Extreme Gradient Boosting algorithm, the classification accuracy at the level of 58% was achieved. Analysing the public sentiment as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like the stock exchange. - Sentiment-Analysis-of-Text-Data-Tweets-/data/train.csv at master · ajayshewale/Sentiment-Analysis-of-Text-Data-Tweets

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