import spacy import pandas as pd import streamlit as st from preprocessing import preprocess_reviews from aspects_extraction import extract_aspects from clustering import cluster_aspect_terms import plotly.express as px import matplotlib.pyplot as plt defaultCsv = { 'Serco USB Hub + Sound Card': 'reviews.csv', 'Honey': 'reviews_honey.csv', } st.set_page_config( page_title="Actionble Insights From Reviews", layout="wide", ) @st.cache def load_reviews(uploaded_file=None, default_file=None): if default_file is not None: reviews = pd.read_csv(default_file) if uploaded_file is not None: reviews = pd.read_csv(uploaded_file) reviews = validate_reviews_dataframe(reviews) return preprocess_reviews(reviews) def validate_reviews_dataframe(r): if 'title' not in r.columns: raise ValueError("column title is required") if 'review' not in r.columns: raise ValueError("column review is required") if 'rating' not in r.columns: raise ValueError("column rating is required") if r['title'].dtype != 'O': raise ValueError("column title must be string") if r['review'].dtype != 'O': raise ValueError("column review must be string") if r['rating'].dtype != 'float64': raise ValueError("column rating must be float") r = r.dropna() if ((r['rating'] < 0) & (r['rating'] > 5)).any(): raise ValueError("values in column rating must be between 0 and 5") return r @st.cache(allow_output_mutation=True, suppress_st_warning=True) def load_model(): return spacy.load("en_core_web_lg") @st.cache(allow_output_mutation=True, suppress_st_warning=True) def get_aspects(reviews): nlp = load_model() return extract_aspects(nlp, reviews) @st.cache(allow_output_mutation=True, suppress_st_warning=True) def cluster_aspects(aspects): nlp = load_model() replacements = cluster_aspect_terms(nlp, aspects) aspects['aspect'] = aspects['aspect'].map(replacements) return aspects def get_aspects_with_ratings(aspects, reviews): aspect_with_ratings = pd.merge(aspects, reviews[['rating']], left_on='review_id', right_index=True) aspect_with_ratings['review_sentiment'] = pd.cut(aspect_with_ratings['rating'], bins=[0, 3, 4, 5], right=True, labels=['Negative', 'Neutral', 'Positive'] ) return aspect_with_ratings def get_aspect_treemap(aspects): treemap = px.treemap(aspects.groupby(['aspect', 'opinion']).size().reset_index(), path=[px.Constant('Aspects'), 'aspect', 'opinion'], values=0, ) treemap.update_layout(margin = dict(t=0, l=0, r=0, b=0)) return treemap def plot_pain_points(aspect_with_ratings): pain_points = (aspect_with_ratings .query('review_sentiment == "Negative"') .groupby('aspect') .size() .sort_values(ascending=False)[:10] ) fig = px.bar(pain_points) fig.update_layout(margin = dict(t=0, l=0, r=0, b=0)) fig.update_traces(marker_color='red', showlegend=False) return fig def plot_gain_points(aspect_with_ratings): gain_points = (aspect_with_ratings .query('review_sentiment == "Positive"') .groupby('aspect') .size() .sort_values(ascending=False)[:10] ) fig = px.bar(gain_points) fig.update_layout(margin = dict(t=0, l=0, r=0, b=0)) fig.update_traces(marker_color='green', showlegend=False) return fig def plot_sentiment_by_aspect(aspect_with_ratings, top=15): pivot = pd.crosstab( index=aspect_with_ratings['aspect'], columns=aspect_with_ratings['review_sentiment'], margins=True, ).sort_values(by='All', ascending=False).iloc[1:, :-1] fig = px.bar(pivot[:top], barmode='group', color_discrete_map={ 'Positive': 'green', 'Negative': 'red', 'Neutral': 'blue', }) fig.update_layout(margin = dict(t=0, l=0, r=0, b=0)) return fig st.write("## Actionble Insights From Reviews") st.write(""" Key to building a successfull product is understanding what users want and what users don't want. This insight can be useful in serveral ways. 1. Designing product that users actually want. 2. Fixing defects in product or addressing users pain points. 3. Staying ahead of the competition. There are millions of reviews that people leave on sites like amazon, tripadvisor etc. To gain insights from this data, you could either read all the reviews one by one or let machine analyze these reviews and find main topics that user care about. """) st.write("## Extracting Aspect Opinion Pairs") st.write(""" Let's say the customer wrote, `The material of the shirt is not soft`. Here `material` is the `aspect` of shirt and `not soft` is the users `opinion` about this aspect. The analyzer finds aspect opinion pairs from the reviews. """) st.write("### Customer Reviews") st.write(""" Dataframe containing reviews of the customer. Title, review, and rating columns are required """) st.sidebar.title("Select Reviews File") default_file = st.sidebar.selectbox( "Choose Sample File", defaultCsv.keys(), ) if default_file is not None: default_file = defaultCsv[default_file] st.sidebar.write("
or
", unsafe_allow_html=True) uploaded_file = st.sidebar.file_uploader( 'Choose a CSV File', type='csv', ) st.sidebar.write("CSV with title(string), review(string) and ratings(float 0-5) columns") try: reviews = load_reviews(uploaded_file, default_file) st.write(reviews) aspects = get_aspects(reviews) aspects = cluster_aspects(aspects) aspects_with_ratings = get_aspects_with_ratings(aspects, reviews) st.write("### Extracted Aspect Opinion Pairs") st.write(""" Treemap of aspect opinion pairs extracted from reviews, treemap is sized according to number of reviews. """) st.plotly_chart(get_aspect_treemap(aspects), use_container_width=True) st.write("### Pain Points And Gain Points") col1, col2 = st.columns(2) with col1: st.write('Top Pain Points (by number of -ve reviews)') st.plotly_chart(plot_pain_points(aspects_with_ratings), use_container_width=True) with col2: st.write('Top Gain Points (by number of +ve reviews)') st.plotly_chart(plot_gain_points(aspects_with_ratings), use_container_width=True) st.write("### Sentiment for each aspect") st.write('(0-3 Negative) (4 Neutral) (5 Positive)') st.plotly_chart(plot_sentiment_by_aspect(aspects_with_ratings), use_container_width=True) except ValueError as e: st.error(e)