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SecLists is the security tester's companion. It's a collection of multiple types of lists used during security assessments, collected in one place. List types include usernames, passwords, URLs, sensitive data patterns, fuzzing payloads, web shells, and many more. - SecLists/Discovery/Web-Content/raft-small-words.txt at master · danielmiessler/SecLists
SecLists/Discovery/Web-Content/raft-small-words.txt at master · danielmiessler/SecLists · GitHub
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
Sentiment-Analysis-of-Text-Data-Tweets-/data/train.csv at master · ajayshewale/Sentiment-Analysis-of-Text-Data-Tweets- · GitHub
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
Automatically exported from code.google.com/p/fuzzdb - fuzzdb/discovery/PredictableRes/raft-small-words-lowercase.txt at master · cdownschrome/fuzzdb
fuzzdb/discovery/PredictableRes/raft-small-words-lowercase.txt at master · cdownschrome/fuzzdb · GitHub
