How Does Sentiment Analysis Work?

Sentiment analysis is a powerful tool used by businesses and organizations to understand the emotions and attitudes expressed within text.

It is a subset of natural language processing (NLP), which itself falls under the broader field of artificial intelligence (AI).

By analyzing the sentiment and opinions expressed within text, businesses can make data-driven decisions, enhance customer experiences and refine their marketing strategies.

What is sentiment analysis?

Sentiment analysis involves the use of natural language processing to automatically identify and classify opinions expressed within a passage of text.

This analysis will determine whether the sentiment expressed is positive, negative or neutral.

The goal is to interpret the emotion behind the text, which can be used for a number of business purposes such as gauging public perception, customer satisfaction or emerging trends.

Technologies used in sentiment analysis

The core technology used in sentiment analysis is natural language processing (NLP).

This technology bridges the gap between computers and human language, enabling computers to read, understand and analyze the meaning of human language expressed through text.

Some of the specific NLP technologies and processes used within sentiment analysis include:

Tokenization

Tokenization involves the breaking down of text into smaller units such as words or sentences. It is a critical early stage step when performing sentiment analysis, helping to identify the individual components of the text that needs to be analyzed.

Tagging

The tagging process will assign a tag to each token based on the context of the word or phrase, identifying whether the token is a noun, verb or adjective.

Parsing and dependency trees

These techniques are used to analyze the sentence structure and to determine the relationship between words used in the text. Understanding this syntax is vital to interpret the sentiment expressed within complex sentences.

Machine learning

Machine learning algorithms detect patterns and make predictions based on data.

For sentiment analysis, these machine learning algorithms are trained on labeled datasets of text labeled with either positive, negative or neutral sentiment.

The machine learning training process involves supervised and unsupervised learning periods. During supervised learning, the training data is labeled to teach the model how to classify the new, unseen text.

Unsupervised learning models will analyze data without pre-existing labels, grouping similar data together based on similarities without classification. This can be used to identify trends and patterns within the sentiment data.

Deep learning

Deep learning is a subset of machine learning. It uses neural networks to learn complex patterns within data, as explained in further detail here. Deep learning models are highly effective for sentiment analysis due to their ability to capture nuanced language features.

Different approaches to sentiment analysis

There are different techniques to performing sentiment analysis. Depending on the use case, dataset and resources available, these techniques vary in suitability and effectiveness.

Lexicon based sentiment analysis

One of the more straightforward approaches, lexicon-based sentiment analysis relies upon a predefined dictionary of words associated with positive, negative and neutral sentiment.

Words such as “excellent” or “love” might be tagged as positive, whereas “hate” or “terrible” might be tagged as negative.

This method works well if the sentiment is directly tied to specific keywords, but struggles with context, sarcasm or more nuanced use cases.

Machine learning based sentiment analysis

Machine learning sentiment analysis involves the models learning from labeled datasets, and trained to detect and recognize patterns associated with different sentiment.

Hybrid approaches will combine rule based lexicon analysis with machine learning, to leverage the strengths of both approaches whilst aiming to mitigate the potential drawbacks.

Using a hybrid approach often involves using lexicon analysis for the initial classification, before utilizing machine learning algorithms to perform the more refined sentiment detection.

Aspect based sentiment analysis

This form of sentiment analysis goes beyond general sentiment classification by breaking text down into specific aspects.

In the context of a food review, this would break the text down into specific aspects such as the “food” and “service”. This approach is particularly useful to businesses who require more detailed analysis.

Transformers

Using models such as BERT (bidirectional encoder representations from Transformers) has enabled a higher degree of accuracy for sentiment analysis solutions, providing a greater understanding of context and semantics.

Challenges in sentiment analysis

Many of the challenges in developing sentiment analysis solutions derive from the various ambiguities and meanings that words have depending on the context.

A primary example is the word “sick”, that in certain cases will be a strongly negative word expressing someone is unwell, whereas in other more colloquial contexts it can mean something is very cool.

Irony and sarcasm is another of the greatest challenges for sentiment analysis. Basic models often struggle to detect the negative context of phrases such as “Oh great, another delay!”.

Multilingual sentiment analysis adds another layer of complexion, with different languages all having distinct structures, idioms and cultural expressions.

Sentiment analysis models will need to be trained in various languages to handle this diversity effectively, rather than simply relying upon translation services.

Custom NLP solutions

Overcoming these sentiment analysis challenges often require custom solutions. These custom solutions require their own datasets and model training to deliver a solution that understands the relevant context and executes the desired outcome to a high level of accuracy.

Sentiment analysis has numerous general applications that can then be customized on creation to allow for unique features. Popular use cases include:

• App review monitoring - gather, analyze, and monitor in one place what customers are saying about your app or a competitor to gain crucial insights.

• Market sentiment monitoring - review in real time the popularity and feeling towards the stock and crypto market, including individual entities, via the sentiment analysis of textual data from social platforms and news articles.

• Virtual assistant chatbots - improve the efficiency and success of an online service with textual data derived from queries that are then processed into an automated service output.

NetGeist specializes in creating tailor-made NLP tools, adapting to your needs. No job is too big, our goal is to develop customized NLP solutions that fit within the concept of your company.

Let us simplify the textual tasks with a unique solution, tailored specifically just for your business.

Contact us to discuss an NLP custom solution.