Here you must have noticed the problem - why does the word "anger" still score positive when it expresses such a strong negative emotion?
This is because SnowNLP and textblob have different scoring methods. SnowNLP's sentiment analysis takes the value of "the probability that the sentence represents a positive sentiment". That is, for the phrase "I'm angry today", SnowNLP believes that it has a very, very low probability of expressing a positive emotion.
It makes so much more sense to explain it that way.
It's fun to learn the basic moves, isn't it? Below you can find some Chinese and English texts to practice sentiment analysis on your own.
But you may run into problems soon. For example, you type in some explicitly negative emotion statement and get a positive result.
Don't think you've been fooled again. Let me explain what the problem is.
First, the sentiment determination of many utterances requires context and background knowledge, so if this type of information is lacking, the correct rate of discrimination will be affected. This is where man is more powerful than machines (at least for now).
Second, any sentiment analysis tool is, in fact, trained. What text material is used for training has a direct impact on the adaptation of the model.
SnowNLP, for example, whose training text is the review data. Therefore, you should have good results if you use it to analyze Chinese comment messages. However, if you use it to analyze other types of text - such as fiction, poetry, etc. - the results are much less effective. Because such a way of combining text data it has not been seen before.
The solution, of course, is to train it with other types of text. I've seen a lot of them, so I'm used to seeing them. As for how to train, please contact the author of the relevant software package for advice.
In addition to the text analytics application areas mentioned in this paper， What other tasks do you know of that could be automated with sentiment analysis to assist in doing？ except forTextBlob harmonySnowNLP outside， What other open and free software packages do you know that can help us with sentiment analysis？ Feel free to leave a comment and share it with everyone， We'll talk together. discussions。
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