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Questions - sentiment-analysis

How do I capture output from rasa chatbot?

I have a working Rasa chatbot that does some simple sentiment analysis using NLTK (vader_lexicon) on the user input. This is done similar to this tutorial: https://rasa.com/blog/enhancing-rasa-nlu-wit...
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Olivia

python

nltk

chatbot

sentiment-analysis

rasa

Votes: 0

Answers: 0

Extract Both Negation & 3 Following Words ( Python/DataFrame)

I'm currently trying to extract both the negation word and 3 words following the negation word. i.e.) "I don't want to visit again. no sympathy." (from a column called ReviewText2) what I wa...
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Willie

python

regex

text

nlp

sentiment-analysis

Votes: 0

Answers: 1

Latest Answer

You can use rx = r'\b(?:{})\b(?:\s+\w+){{0,3}}'.format(query) Negative_Reviews['negation'] = Negative_Reviews['ReviewText2'].str.findall(rx) The regex will look like \b(?:doesn't|don't|without|won't|...
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Wiktor Stribiżew

'list' object has no attribute 'lower''list' object has no attribute 'lower' in TfidfVectorizer

i have csv file about some arabic tweet i did token and stemming and clean the text `data = pd.read_csv(r"tweet.csv" ,dtype=str, encoding="utf-8") def preprocessing(text): \...
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Djihad Jiji

python

nlp

vectorization

sentiment-analysis

tfidfvectorizer

Votes: 0

Answers: 1

Latest Answer

If the 'text' is in a list format, the lower function does not work for a list. It works for a string as an element in a list. So you should input [x.lower() for x in text] as an argument for the fun...
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user67275

Input 0 of layer "bidirectional_2" is incompatible with the layer: expected ndim=3, found ndim=2

I am trying to classify text with bi-lstm but while I run model.predict on new dataset it is giving me this error: Input 0 of layer "bidirectional_2" is incompatible with the layer: expected...
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Rubiya shoukat

python

tensorflow

classification

sentiment-analysis

bilstm

Votes: 0

Answers: 1

Latest Answer

you should use Reshape layer after Bidirectional layer This might works: model = Sequential() model.add(Embedding(len(word_index) + 1, embed_size, weights=[embedding_matrix])) model.add(Bidirectional(...
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Arman Asgharpoor

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