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import streamlit as st |
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import spacy |
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from spacytextblob.spacytextblob import SpacyTextBlob |
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st.set_page_config(layout='wide', initial_sidebar_state='expanded') |
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st.title('Text Analysis using Spacy Textblob') |
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st.markdown('Type a sentence in the below text box and choose the desired option in the adjacent menu.') |
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side = st.sidebar.selectbox("Select an option below", ("Sentiment", "Subjectivity", "NER")) |
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Text = st.text_input("Enter the sentence") |
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@st.cache |
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def sentiment(text): |
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nlp = spacy.load('en_core_web_sm') |
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nlp.add_pipe('spacytextblob') |
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doc = nlp(text) |
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if doc._.polarity<0: |
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return "Negative" |
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elif doc._.polarity==0: |
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return "Neutral" |
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else: |
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return "Positive" |
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@st.cache |
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def subjectivity(text): |
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nlp = spacy.load('en_core_web_sm') |
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nlp.add_pipe('spacytextblob') |
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doc = nlp(text) |
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if doc._.subjectivity > 0.5: |
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return "Highly Opinionated sentence" |
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elif doc._.subjectivity < 0.5: |
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return "Less Opinionated sentence" |
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else: |
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return "Neutral sentence" |
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@st.cache |
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def ner(sentence): |
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nlp = spacy.load("en_core_web_sm") |
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doc = nlp(sentence) |
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ents = [(e.text, e.label_) for e in doc.ents] |
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return ents |
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def run(): |
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if side == "Sentiment": |
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st.write(sentiment(Text)) |
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if side == "Subjectivity": |
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st.write(subjectivity(Text)) |
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if side == "NER": |
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st.write(ner(Text)) |
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if __name__ == '__main__': |
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run() |
