sentiment-analysis

I've been reading a lot of articles that explain the need for an initial set of texts that are classified as either 'positive' or 'negative' before a sentiment analysis system will really work. My question is: Has anyone attempted just doing a rudimentary check of 'positive' adjectives vs 'negative' adjectives, taking into account any simple negators to avoid classing 'not happy' as positive? If so, are there any articles that discuss just why this strategy isn't realistic? A classic paper by Peter Turney (2002) explains a method to do unsupervised sentiment analysis (positive/negative

How do I handle/get rid of emoticons so that I can sort tweets for sentiment analysis? Getting: Error in sort.list(y) : invalid input Thanks and this is how the emoticons come out looking from twitter and into r: \xed��\xed�\u0083\xed��\xed�� \xed��\xed�\u008d\xed��\xed�\u0089 This should get rid of the emoticons, using iconv as suggested by ndoogan. Some reproducible data: require(twitteR) # note that I had to register my twitter credentials first # here's the method: http://stackoverflow.com/q/9916283/1036500 s <- searchTwitter('#emoticons', cainfo="cacert.pem") # convert to data frame df <-

All I want to do is find the sentiment (positive/negative/neutral) of any given string. On researching I came across Stanford NLP. But sadly its in Java. Any ideas on how can I make it work for python? Use py-corenlp Download Stanford CoreNLP The latest version at this time (2018-10-23) is 3.9.2: wget https://nlp.stanford.edu/software/stanford-corenlp-full-2018-10-05.zip https://nlp.stanford.edu/software/stanford-english-corenlp-2018-10-05-models.jar If you do not have wget , you probably have curl : curl https://nlp.stanford.edu/software/stanford-corenlp-full-2018-10-05.zip -O https://nlp

Hell everyone! I'm using the Stanford Core NLP package and my goal is to perform sentiment analysis on a live-stream of tweets. Using the sentiment analysis tool as is returns a very poor analysis of text's 'attitude' .. many positives are labeled neutral, many negatives rated positive. I've gone ahead an acquired well over a million tweets in a text file, but I haven't a clue how to actually train the tool and create my own model. Link to Stanford Sentiment Analysis page "Models can be retrained using the following command using the PTB format dataset:" java -mx8g edu.stanford.nlp.sentiment