What is Sentiment Analysis and Why Does it Matter?
Sentiment analysis, also known as opinion mining, refers to the application of natural language processing (NLP) and machine learning to identify and extract subjective information from written language. The goal is to determine if the underlying sentiment expressed is positive, negative or neutral.
Being able to automatically classify sentiment at scale is incredibly valuable for businesses. Consumer opinions shared online shape brand perception, so monitoring textual data like social media posts, product reviews and customer support tickets for sentiment provides real-time insight into how people feel about your company, offerings or campaigns. This enables data-driven decisions around reputation management, product improvements, and growth opportunities.
According to industry surveys, over 80% of enterprises now leverage sentiment analysis solutions, with 95% of these organizations reporting significant benefits including better customer understanding, faster issue resolution and increased revenue.
Key Capabilities
Here are some key capabilities provided by sentiment analysis:
- Polarity classification – Determining if sentiment is positive, negative or neutral
- Aspect-based analysis – Linking sentiment to specific product features and topics
- Emotion detection – Identifying feelings like happiness, sadness, anger
- Intent analysis – Understanding motivations and goals behind feedback
With modern machine learning approaches, sentiment analysis can provide granular and accurate insights from unstructured text data at scale. Now let‘s explore some techniques for gathering consumer sentiment data.
Gathering Data for Sentiment Analysis
There are several common methods for collecting opinion-based text data for input into sentiment analysis systems:
Surveys & Interviews
Directly prompting consumers to share feedback on products, experiences and perceptions through surveys and interviews elicits subjective perspectives you may not otherwise find organically on social media or review sites.
Web Scraping
User-generated reviews on sites like Amazon, Yelp and industry forums are ripe targets for scraping to compile opinionated text on specified topics. Always check a site‘s terms to avoid violations.
Social Media Monitoring
Hashtag tracking and working with platforms‘ API allows you to harvest tweets, posts and comments containing relevant product or brand mentions.
Customer Support Logs
Tickets contain subjective commentary on issues and pain points experienced when engaging with products and services.
Blending data from multimedia sources provides breadth and depth for robust sentiment analysis. Now let‘s explore popular techniques.
Sentiment Analysis Approaches
There are 3 primary approaches for performing sentiment analysis:
Lexicon-Based
This methodology relies on a predefined sentiment lexicon – a dictionary containing words/phrases with positive, negative or neutral polarities. Text is classified by aggregating the sentiment scores of constituent terms.
Simple and transparent, but lacks granular understanding of linguistic context. Performace limited by lexicon coverage.
Machine Learning Models
ML classifiers like logistic regression and recurrent neural networks are trained on large datasets of text labeled with sentiment. Models learn contextual word/phrase associations and can adapt to informal language use cases like social media.
Challenging to create quality training data. Requires careful feature engineering and hyperparameter tuning.
Hybrid
Combines a rules-based lexicon approach to baseline classification, enhanced by an ML model to improve accuracy through contextual inference. This blended technique is growing in popularity.
Gains robustness over individual methods, but more complex to implement.
Machine learning has clearly emerged as the most accurate modern approach. Now let‘s do a deeper walkthrough of the process.
Building a Machine Learning Sentiment Classifier
Constructing an ML model for text classification involves several key steps:
Data Gathering & Annotation
Compile a dataset containing text excerpts and manually label the overall sentiment of each example as positive, negative or neutral. High-quality human-annotated data is essential for supervision.
Data Cleaning & Preprocessing
Prepare the text for machine consumption through normalization, removing stop words, correcting spellings, expanding contractions etc. This improves downstream model performance.
Feature Extraction & Vectorization
Algorithms require numerical input vectors. Techniques like bag-of-words, TF-IDF and word embeddings convert text into informative numeric representations capturing word frequencies, relationships etc.
Training & Evaluation
Feed vectorized text features into chosen model like Naive Bayes, LSTM or BERT pretrained neural network with associated sentiment labels. Assess accuracy on held-out test set.
Prediction
Inference on new unlabeled examples by appending predicted sentiment tags based on learned text associations.
Through incremental training iterations, robust and scalable sentiment classifiers can be developed. Next let‘s explore some common challenges.
Sentiment Analysis Challenges
While machine learning has greatly expanded capabilities, several hard problems remain:
Sarcasm Detection
Subtleties around sarcasm and irony confuse algorithms reliant on taking text literally. Advances in Transformer language models show promise by incorporating conversational context.
Aspect-Based Sentiment
Broad sentiment measures can miss important nuances. Linking identified sentiment with specific product features, topics and entities enables more impactful analysis.
Data Quality Issues
Noisy or biased data cripples model performance. Gathering balanced, representative samples for training requires a thoughtful approach.
Ongoing research around neural techniques continues to push boundaries of accuracy. But access to ample quality training data remains the fundamental challenge.
Now let‘s survey some leading sentiment analysis tools and services.
Top Sentiment Analysis Solutions
| Platform | Description | Accuracy | Language Support |
|---|---|---|---|
| Google Cloud NLP | Pretrained models for sentiment and entity analysis | 89% | English, Spanish, Japanese |
| AWS Comprehend | Managed sentiment classification via cloud platform | 91% | English, Spanish, French, German, Italian |
| MeaningCloud | Specialized multilingual sentiment and emotion API | 86% | English, Spanish, French +9 more |
| TalkWalker | AI-powered social media monitoring with sentiment | 84% | Any language |
| Roamler | Customizable enterprise text analytics | 88% | English, Chinese, Arabic +17 more |
There are also many excellent open source Python libraries like TextBlob, VADER and Flair. Overall sentiment analysis capabilities are now accessible via simple APIs for ease of integration into business applications, while still achieving high accuracy.
Case Studies
Here are a few examples of impact unlocked by implementing sentiment analysis:
Monitoring Brand Health
A leading telecom firm uses sentiment tracking to gain early warning signs into dips for key brand perception metrics like value, customer service and network quality sentiment. This enables rapid response to mitigate crises.
Informing Product Roadmaps
A software company analyzes user reviews and feedback for common themes and pain points to quantify level of dissatisfaction with certain product capabilities. These insights feed into priority ranking for roadmap development.
Dynamic Advertising
A sports betting service adjusts digital ad messaging and creative in real-time based on shifting sentiment detected towards leagues and teams on social channels and forums. This optimizes engagement and conversions.
The Future of Sentiment Analysis
While already providing immense value today, continued advances promise to expand capabilities even further:
- More contextual neural approaches tackling sarcasm, irony and aspect-based analysis
- Cross-lingual transfer learning enabling portable models across languages
- Generative sentiment classifiers that can explain their predictions
- Analysis evolving from document to conversation scale
With exponential growth in opinionated online content from consumers, the importance of sentiment analysis for businesses will only increase in years to come. The technology and techniques covered in this definitive guide should empower you to get started.
