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What is Sentiment Analysis? Tools and Uses

What is Sentiment Analysis? Tools and Uses

Sentiment analysis, sometimes called opinion mining, is an exciting field within natural language processing (NLP). It has seen rapid growth and evolution over the past decade. So, what exactly does sentiment analysis entail? At a basic level, it involves classifying text – anything from social media posts to survey responses to product reviews – as positive, negative, or neutral. However, modern sentiment analysis solutions are capable of much more nuanced than those three categories.

Core Concepts and Applications

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Sentiment analysis systems can identify whether a statement is positive or negative and the degree of positivity or negativity. They can extract the specific emotions expressed, categorise reviews, detect irony or sarcasm, and summarise the overall sentiment towards brands, products, individuals, organisations, events, locations, creative works, and more.

The applications of sentiment analysis span countless industries and use cases. It powers recommendation systems, guides marketing strategies and campaigns, enables better customer service, and provides insight into brand health tracking and reputation management. It is also widely used in financial markets to make trading decisions and predict stock performance. Other typical applications include public health monitoring, understanding political affiliation, improving human-computer interaction, and enhancing defence systems.

A Thriving Interdisciplinary Field

Sentiment analysis combines various domains – computational linguistics, text analysis, data mining, machine learning, deep learning, natural language processing, and biometrics. It is an interdisciplinary field, informed by both computer and social sciences.

Research in sentiment analysis also intersects with behavioural economics and finance, political science, health communication, literary analysis, and more. So, while it is situated within computer science and AI, sentiment analysis has connections across the sciences, engineering, humanities, and professional spheres.

Key Tasks and Techniques

Sentiment analysis comprises several critical tasks accomplished through a variety of techniques. Here, we summarise the integral tasks and explore some essential approaches.

Integral Tasks

Sentiment analysis solutions must perform two essential jobs:

  • Subjectivity classification – determining if a statement is objective or subjective
  • Polarity classification – detecting whether subjective views are positive, negative, or neutral

Additionally, many sentiment analysis systems take on tasks like:

  • Detecting degrees of positivity or negativity
  • Categorising reviews (e.g. food, product, movie reviews)
  • Identifying emotional states beyond positive/negative, like joy, sadness, anger
  • Detecting sarcasm and irony
  • Summarising sentiment about brands, products, services, individuals, organisations, events, etc.
  • Tracking sentiment over time
  • Comparing sentiment across demographics
  • Explaining what factors drive positive or negative sentiment
Sentiment Analysis: Mining Opinions, Sentiments, and Emotions (Studies in Natural Language Processing)
  • Hardcover Book
  • Liu, Bing (Author)
  • English (Publication Language)
  • 448 Pages – 11/12/2020 (Publication Date) – Cambridge University Press (Publisher)

Key Techniques and Methods

There are several major approaches used to build sentiment analysis systems:

  • Lexicon-based – Uses sentiment lexicons containing words/phrases labelled as conveying positive sentiment, negative sentiment, etc.
  • Machine learning – Trains statistical machine learning models like logistic regression on labelled sentiment data.
  • Deep learning – Leverages neural networks to understand the sentiment in text based on learnt semantics.
  • Hybrid – Combines machine learning and deep learning with lexicon-based approaches.
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Additionally, here are some other notable techniques:

  • Aspect-based – Detects sentiment towards specific attributes of products/services
  • Multilingual – Handles multiple languages like English, French, and Chinese
  • Multimodal – Incorporates non-textual data like images, audio, video
  • Transfer learning – Leverages models pre-trained on huge text entities

Later sections provide more details on these approaches and innovations in sentiment analysis methodology. First, let’s explore the building blocks that enable sentiment analysis.

Foundations of Sentiment Analysis

Sentiment Analysis Guide

Sentiment analysis has foundations across linguistics, text processing, data science, machine learning, and evaluation metrics. Here, we introduce fundamental concepts that serve as building blocks for sentiment analysis systems.

Linguistic Foundations

  • Parts-of-speech – Nouns, verbs, adjectives, adverbs, etc. Adjectives frequently convey sentiment.
  • Negation handling – Negation words like “wouldn’t” can flip sentiment orientation.
  • Word sense disambiguation – Determining word meaning from context to assess connotative sentiment.

Text Processing

  • Tokenisation – Splitting text into words, phrases, symbols, etc.
  • Normalisation – Converting text to the typical case, stripping punctuation/whitespace, etc.
  • Stopword removal – Removing frequent words like “and” or “the” that rarely convey sentiment.

Feature Engineering

  • N-grams – Contiguous word sequences useful for capturing context.
  • TF-IDF vectors – Statistic reflecting term importance to document in a corpus.
  • Word embeddings – Encode words/phrases into dense vector representations capturing meaning.

Machine Learning Models

  • Logistic regression – Predicts probabilities of class membership.
  • Naive Bayes classifier – Probabilistic model based on Bayes Theorem.
  • SVM – Finds optimal line/plane to separate classes.
  • RNNs – Recurrent neural network architecture well-suited for sequence data.

Evaluation Metrics

  • Accuracy – Percentage of correct polarity classifications.
  • Precision – Positive precision measures the percentage of identifying true positives.
  • Recall – Percentage of actual positive cases correctly classified
  • F1 scores – Balance between precision and recall.

These concepts establish the foundation for the sentiment analysis techniques explored next.

Sentiment Analysis in Social Networks
  • Pozzi, Federico Alberto (Author)
  • English (Publication Language)
  • 284 Pages – 09/30/2016 (Publication Date) – Morgan Kaufmann (Publisher)

Key Sentiment Analysis Techniques

Now that we’ve covered sentiment analysis's integral tasks and foundations let’s dig deeper into prevailing techniques leveraged today.

Lexicon-Based Approaches

Lexicon-based techniques rely on sentiment lexicons – dictionaries containing words/phrases annotated with polarity scores. For example:

TermPolarity Score

Lexicons can assign polarity scores at various linguistic levels:

Document Level

Aggregate word-level polarity scores to determine overall document sentiment. For example:

This hotel had beautiful views, but the tacky decor ruined the experience.

Positive words: {wonderful: +5}

Negative words: {garish: -3; ruined: -2}

  • -5 negative points
  • +5 positive points
  • Net Negativity

Challenges with lexicon methods include handling negation and accounting for shifts in word polarity based on context. Many lexicons also have limited vocabulary coverage.

Machine Learning Approaches

Machine learning classifiers for sentiment analysis are trained on labelled datasets containing text samples annotated with their sentiment. Common models include:

  • Logistic regression
  • Naive Bayes
  • Support Vector Machines (SVMs)

For example, an SVM model might be trained to classify movie reviews as positive or negative based on word n-gram features:

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Review TextLabeled Sentiment
This film was imaginative and impactful.Positive
The plot was dull and predictable.Negative

Once trained, these models can classify the sentiment of new unlabeled reviews. Machine learning approaches excel at incorporating features that capture negation, shifts in word sense based on context, etc. However, they rely on quality training data.

Deep Learning Methods

Deep learning architectures like recurrent neural networks (RNNs) now achieve state-of-the-art results on sentiment analysis with minimal data pre-processing. For example, long short-term memory networks (LSTMs) can understand sentiment based on learnt semantics instead of just word order/frequency statistics.

Deep learning methods learn distributed vector representations capturing meaning, which allows them to generalise accurately even with limited labelled training data in some cases. However, performance is highly dependent on model architecture and hyperparameter tuning.

Hybrid Approaches

Many of today’s top-performing sentiment analysis solutions ensemble deep learning models like LSTMs with lexicon-based features and machine learning classifiers. Such hybrid approaches use their strengths to handle challenges like sarcasm and figurative language.

Additionally, transfer learning is growing in popularity, where models pre-trained on massive text corpora are fine-tuned for sentiment analysis tasks. This leverages their acquired linguistic knowledge to achieve better generalisation.

Next, we survey some compelling new frontiers in sentiment analysis methodology.

New Frontiers in Methodology

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Sentiment analysis remains an active research area with exciting innovations across various techniques. Here, we explore a few promising frontiers.

Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis classifies sentiment towards attributes or “aspects” of entities like products, services, organisations, etc., rather than overall sentiment.

For example, consider the hotel review:

“The room was clean, but the staff seemed rude and inattentive.”

Overall polarity = negative

But for critical aspects:

  • Room cleanliness: Positive
  • Staff friendliness: negative

Aspect-level analysis provides more nuanced insight for reputation monitoring and decision-making. Unique challenges include associating sentiment with the correct entity/aspect in long reviews with many targets.

Multimodal Sentiment Analysis

Most techniques focus exclusively on text, but multimodal sentiment analysis additionally considers images, audio, and video. This can help better understand sentiment from social media posts, vlogs, broadcasts, and other media.

For example, text comments on a YouTube video may convey positive sentiment, but negative vocal inflexions in the audio could indicate sarcasm. Fusing sentiment signals from all modalities provides a richer perspective.

Temporal Sentiment Analysis

Aggregate opinions fluctuate dynamically across time. Temporal sentiment analysis tracks how sentiment around entities evolves hourly, daily, or weekly.

For example, public sentiment towards politicians can shift quickly in response to scandals, speeches, policy changes, etc. Temporal analysis helps explain spikes and dips.

Time series forecasting models can also predict future sentiment levels based on historical patterns. This is valuable for anticipating reputation risk.

Explainable Sentiment Analysis

Many advanced machine learning models act as “black boxes”, providing no visibility into why specific sentiment predictions were made. Explainable AI solutions for sentiment analysis shed light on model behaviour by exposing influential features, textual rationales linked to predictions, counterfactuals, and more.

For example, highlighting the excerpt “ruined the whole experience” explains why a negative classification was made on a hotel review, building appropriate trust.

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Real-World Deployment Considerations

Sentiment analysis delivers tremendous value across many domains when thoughtfully productionised. Here, we review critical factors for applied deployments.

Data Imbalance Handling

Real-world sentiment data often exhibits class imbalance, where one sentiment type is far more prevalent. For example, product reviews tend to be overwhelmingly positive. Models can become biased, always predicting the majority class. Oversampling minority classes or penalising models for misclassifying these cases helps address the imbalance.

Domain Adaptability

Language use and context vary enormously across different verticals like social media, financial news, healthcare surveys, etc. Solutions pre-trained on generic text often need to be generalised. Adaptation techniques like transfer learning enable specialisation to new domains.

Data Drift Handling

Language evolves dynamically, yet models deployed to production can decay in accuracy as data drifts. Continual learning methods identify when distributions begin to shift and trigger retraining to maintain robustness.

Cloud Scalability

Sentiment analysis workloads often involve processing high volumes of streaming text from multiple sources: serverless platforms scale dynamically, analysing sentiment across thousands of posts per second without overhead.

By considering challenges like class imbalance, domain differences, concept drift, and scale, sentiment analysis systems can deliver reliable results in applied settings.

FAQs About Sentiment Analysis

We’ll conclude this overview of sentiment analysis with answers to a few frequently asked questions for additional context:

Is sentiment analysis still an active research area?

Many open challenges remain around detecting complex linguistic phenomena like sarcasm, handling domain differences, and adapting to informal language on social media. So, while core techniques are established, many unsolved problems remain.

What background is required to work in sentiment analysis?

Coursework in natural language processing, machine learning, and data science serves as good preparation. Familiarity with linguistics, like parts of speech and semantics, also provides functional fundamentals. Comfort with programming languages like Python is also a prerequisite.

How accurate are modern sentiment analysis solutions?

State-of-the-art accuracy exceeds 90% on domains with well-formatted text like movie reviews or product surveys. Performance degrades on noisy social media posts and other informal text, though. There is still much progress to be made, especially on robustness.

What is the future outlook for this field?

Sentiment analysis will continue expanding in scope and importance as more text data is generated across sectors. Understanding stakeholder perceptions through opinions shared online enables better decision-making and significant business value, further accelerating adoption.

Are there any limitations or dangers with applying sentiment analysis irresponsibly?

While there is tremendous upside, deployments could perpetuate issues like bias if the assumptions and uncertainties behind a model are not well understood. Practitioners must be ethical, transparent, and aware of limitations.

Hopefully this overview has enhanced your understanding of modern sentiment analysis. Please reach out with any other questions!

Last update on 2024-07-16 / Affiliate links / Images from Amazon Product Advertising API

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Stuart Crawford

Stuart Crawford is an award-winning creative director and brand strategist with over 15 years of experience building memorable and influential brands. As Creative Director at Inkbot Design, a leading branding agency, Stuart oversees all creative projects and ensures each client receives a customised brand strategy and visual identity.

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