As 2021 begins, companies are looking closely at data and insights to inform strategic planning in the face of uncertainty and change. Performing sentiment analysis on customer product reviews provides a valuable window into how people felt about brands, products, and services during the tumultuous events of 2020.
By extracting and analyzing review sentiment, business leaders can identify pain points and opportunities to improve experiences in the year ahead. Here we’ll explore key concepts in sentiment analysis, examine product review datasets, and outline an effective workflow for strategizing with review sentiment insights.
Sentiment Analysis Overview
Sentiment analysis aims to computationally identify and categorize opinions expressed in text into categories like “positive”, “negative” or “neutral”. This provides data-driven insights into the overall sentiment behind messages.
For product reviews, sentiment analysis can determine if a review expresses positive, negative or neutral sentiment towards aspects like product quality, features, use cases, value for money, customer service, and more. Various techniques like text classification, natural language processing (NLP), and machine learning are used to detect sentiment.
Sentiment analysis greatly scales a company’s ability to synthesize insights from customer feedback that would otherwise require manual analysis. It produces quantifiable sentiment data from thousands of reviews in minutes rather than days or weeks.
Beyond just classifying sentiment as positive or negative, advanced techniques can detect emotional tone and nuance, identifyEntitieskeywords, and extract sentiment insights around specific features and topics. This provides more granular insights from unstructured text data.
Review Dataset Overview
Product reviews for sentiment analysis are collected from sources like:
– Ecommerce sites
– App stores
– Review websites
– Social media
Important data fields typically include:
– Review text
– Reviewer name/ID
– Star rating
– Product ID
– Product name
Additional metadata like device type for app reviews can provide added context. The goal is assembling a dataset representative of the broader population of customer reviews.
For established products/services, datasets may contain thousands of reviews collected over months or years. New offerings may have smaller datasets, but these still provide valuable early feedback on market reception.
Preprocessing and Cleaning
Before analysis, raw review text data needs preprocessing. Steps include:
– Removing HTML, URLs, special characters
– Fixing spelling errors
– Expanding contractions
– Converting text to lowercase
– Removing stop words
– Correcting abbreviations
This cleaning ensures the text is standardized for more accurate sentiment detection. Any invalid, duplicate or non-English reviews are filtered out.
A typical workflow for sentiment analysis of product reviews includes:
1. Data Collection – Compile review dataset from sources like websites, surveys, social media.
2. Preprocessing – Clean text data to optimize for analysis.
3. Sentiment Modeling – Apply NLP modeling to categorize sentiment from review text.
4. Analysis – Aggregate results and analyze sentiment polarity scores.
5. Insights – Identify key topics, pain points and opportunities.
6. Strategizing – Incorporate findings into 2021 strategy.
There are various open source libraries and commercial tools available to perform the sentiment modeling step. Options include Python toolkits like TextBlob, Amazon Comprehend, and Google Cloud Natural Language API.
Analysis and Reporting
Sentiment modeling of the preprocessed reviews produces sentiment polarity scores on a scale such as:
– 2 = Very Positive
– 1 = Positive
– 0 = Neutral
– -1 = Negative
– -2 = Very Negative
Aggregating these scores provides totals for positive, negative and neutral reviews. More advanced analysis can summarize sentiment by attributes like star rating, product, timeframe, and reviewer demographics.
Visualizations like pie charts, word clouds, and correlation plots are created to highlight trends and relationships in the data. Power BI, Tableau, Looker and other business intelligence tools can generate interactive sentiment analysis reports for easy insights discovery.
Analyzing sentiment scores over time can reveal how major events like COVID-19 impacted customer opinions and pain points. Brands can also compare sentiment changes for their own products vs competitors.
Key topic modeling identifies features, subjects and use cases that draw frequent positive or negative sentiment. This enables strategizing around enhancing strengths vs improving weaknesses in 2021 plans.
Sentiment differences across customer segments and demographics provides tailored insights for marketing, product design and positioning. Analyzing reviews from cancelled subscribers provides valuable churn reduction insights.
Mining reviews for suggestions, complaints and needs provides voice-of-the-customer insights to guide planning. Companies can align roadmaps around addressing top pain points and requests in 2021.
Case Study: Streaming Service Strategizing
As an example, a video streaming company performs sentiment analysis on 50,000 customer reviews of its service from 2019-2020. The analysis reveals:
– Overall sentiment dropped from +0.15 to -0.12 during 2020.
– Sentiment on pricing increased +0.3 during 2020 price hikes.
– Topic sentiment related to ads/promotions dropped -0.5 over 2020.
– Mobile app sentiment declined -0.4 amid connectivity issues.
– Key topics like kids content, downloads, and originals saw declining sentiment over 2020.
These insights inform strategic priorities for 2021:
– Limit further price increases to avoid churn.
– Enhance ad relevance by applying sentiment insights.
– Improve app stability and performance.
– Invest more in exclusive kids content and downloads.
– Safeguard original content sentiment with more hits.
The company uses these sentiment-guided strategies to better retain and expand its customer base in 2021.
Performing sentiment analysis on customer product reviews provides data-driven insights to guide more informed, customer-centric strategic planning. By extracting insights from thousands of reviews, brands can identify improving or declining pain points and pivot their 2021 strategies accordingly.
Advanced analytic techniques enable rich insights discovery from unstructured text at scale. The case demonstrates how sentiment analysis leads to objective, actionable strategies based on current customer opinions.
With careful analysis design and interpretation, sentiment scoring serves as a competitive advantage for brands strategizing their 2021 roadmaps. Product review sentiment provides a voice-of-the-customer pulse to navigate uncertain times and ever-changing market landscapes.