What is sentiment analysis?

Sentiment analysis refers to one aspect of the natural language processing field and is solely dedicated to understanding subjective opinions or sentiments aggregated from a range of sources on a single subject.

Put in a business context, sentiment analysis refers to tools that identify and extrapolate information from opinions and then enhance business operations. This is done with the help of a range of algorithms that dig into the sub-context of opinions and attempts to understand attitudes with regards to a product or any specific element.

Sentiment analysis is all about opinion mining to understand the reasoning of the general public, which allows businesses to examine product positioning. Sentiment analysis is used in many different areas:

  • Product analytics
  • Market research
  • Hyper-personalization
  • Reputation management
  • Perceptions in public relations
  • Precision targeting of customers
  • Product reviews
  • Product feedback
  • Efficient customer service

Sentiment analysis plays a huge role in helping businesses develop smarter products and services that specifically address customers’ needs.

Why is sentiment analysis important?

Sentiment analysis focuses on the perception of a product and its market desirability through processing sentiment data. There are several resources, public and private, that can be harnessed to get information related to customer perception. These sources include:

  • Customer correspondence concerning a product or service
  • User-generated public reviews
  • Professional media-based product reviews
  • The product’s social media presence such as mentions or hashtags
  • Forum mining, both general and purpose-oriented

With sentiment analysis, businesses can understand their vast amounts of data and transform it into a range of positive outcomes. The benefits include:

  • A clear understanding of an audience’s perception about a product or service
  • An in-depth look at the current market status from a customer’s point of view

For both, the results create a value proposition for a product’s specific audience.

But why is such understanding necessary?

In terms of key performance indicators for any product, understanding the next step in its evolution requires a clear view of its pros and cons. Sentiment analysis is ideal to determine marketing efforts and their direction, as well as business development. With sentiment analysis-based marketing, businesses can understand the strengths and weaknesses of any product from a customer’s viewpoint.

Sentiment analysis also uses real data, which, when analyzed correctly, should provide genuine information to formulate actionable strategies. There is no guessing or what-ifs.

Concerning market research, sentiment analysis is significant but much less integral. It provides an alternate perspective and more variations into what the market wants. It often opens up newer avenues of approach, enabling a business to find an untapped niche for a product.

Sentiment analysis is easy on a basic level, but from an enterprise perspective, you will need elaborate tools for advanced insights.

Types of sentiment analysis

Here is a look at the different types of sentiment analysis. All of these analyses use data-driven artificial intelligence (AI) and machine learning (ML) to make their judgements and predictions:

Fine-grained sentiment analysis

Fine grained sentiment analysis interprets the polarity of public opinion. This analysis can be a simple binary like/dislike sentiment or a positive/negative differentiation, or it can be more complex with deeper specifications such as a likert rating system of 1 to 7 measuring strong agreement to strong disagreement to behavioral questions.

Emotion detection

Emotion-based sentiment analysis detects specific emotional states present in customer correspondence based on language and machine learning algorithms. The results determine why customers feel a certain way about products.

Aspect-based sentiment analysis

Aspect-based sentiment analysis ventures a bit deeper. Its focus is to find out customer opinions on a product’s particular aspect or element (like the latest upgrade to a phone’s software). With aspect-based analysis, it is easy to keep track of how customers perceive the upgrade and what the specific strong or weak points are from a customer's perspective.

Intent analysis

Intent analysis is used in customer support services to enable the streamlining of workflows. It determines the specific intention behind someone’s message.

What are the two approaches to sentiment analysis?

There are two established approaches to sentiment analysis:

Rule-based approach

The rule-based approach uses an algorithm that identifies a detailed and clear opinion description. The approach includes identifying subjectivity, the polarity of opinions, and also the opinion subject. This rule-based approach utilizes basic natural language processing--involving some of these possible operations:

  • Stemming
  • Parsing
  • Tokenization
  • Tagging part of speech
  • Language analysis

The rule-based approach begins with two word sets. One of these sets contains only positives, the other only negatives. The algorithm scans the text thoroughly to look for words that match its predefined rules and word lists and then calculates the most prevalent words. More positive vocabulary means positive polarity and more negative vocabulary indicates negative polarity.

The drawback with rule-based algorithms is that some results are delivered inadequately; there is little flexibility or precision that allows the result to be usable as rule-based approaches do not account for context. However, it can ascertain the tone of the messages, which is useful for customer support.

Rule-based approaches can have issues with linguistics. Slang changes quickly and can present some challenges with aligning words with positive or negative sentiments. Today, rule-based sentiment analysis is often used as a starting point for future implementation and training of machine learning solutions.

Automatic sentiment analysis approach

Automatic sentiment analysis delves deep into text and extracts usable data. Rather than base its work on rules that are pre-defined, automatic sentiment analysis uses machine learning to understand the outline of a message. This automatic approach uses supervised machine learning classification algorithms, increasing the level of precision and accuracy and processing information based on a range of criteria quickly.

Sentiment analysis uses machine learning algorithms to explore data. In general, sentiment analysis can involve the following types of classification algorithms:

  • Linear regression
  • Support vector machines
  • Naive Bayes
  • Recurrent neural network derivatives (such as long short term memory networks and gated recurrent units)

Sentiment is quite tricky because it comes across as being a regular extraction of a particular insight. However, there is a lot of work that goes into getting an accurate gist of the sentiment.

How does sentiment analysis work?

Sentiment analysis is essentially a classification algorithm that aims at discovering opinion-based viewpoints, related emotions, and information that may be of particular interest.

What constitutes an “opinion” in sentiment analysis? Generally speaking, an opinion is a viewpoint that may not be based in fact or accurate knowledge.

But, from a data perspective, there is much more to opinions. While it is a subjective evaluation based on personal experiences, it corresponds with emotions: a set of signifiers present a complex viewpoint of experiences and emotions. With this understanding, sentiment analysis can:

  • Extract sentiment data on one specific platform, such as a review site or customer support desk
  • Determine positive or negative polarity
  • Define whether the subject matter is being spoken of in general or specifically
  • Identify opinion holders individually or in the context of existing audience segments

Sentiment analysis can be used at many different levels:

  1. Document-level: analyze entire texts
  2. Sentence-level: look into a single sentence
  3. Sub-sentence level: ascertain sub-expressions located within a sentence

Because opinions are subjective, there are four sub-categories they can be characterized under:

  1. A direct opinion: where the opinion is to the point
    1. “This application user interface is poor”    
  2. A comparative opinion: where a comparison is drawn between A and B
    1. “The application user interface on app B is worse than app A”
  3. The explicit opinion: where things are made extremely clear
    1. “This app functions optimally”
  4. Implicit opinions: where opinions are merely implied
    1. “The app began crashing within a day”

Common challenges in sentiment analysis operations

Context and polarity

Algorithms have trouble understanding context. While humans can understand the context of an interaction, it can be an obstacle for an algorithm. Therefore, the algorithm will have to be configured so it includes a context component for messages.

Text vectorization solves this issue. It charts out the connections of words in a text (and their relations to others) based on the parts of speech. This brings in an added dimension to text sentiment analysis and ensures a clear understanding of the message’s tone.

Determining subjectivity and tone

Identifying tone in a message is the key feature of sentiment analysis. Analyzing tone can range from simple to complex, depending on the words used. Human interactions can be implicit or explicit and subjective or objective, which is difficult for algorithms to judge.

To solve this, the product’s characterization needs multiple options and relevant categories to help the algorithm accurately determine subjectivity and tone.

Identifying sarcasm and irony

Machines and algorithms have the most trouble comprehending irony and sarcasm. Words used in one sequence can indicate something completely different than another sentence, and algorithms take it all at face value and may get it completely wrong. The way to tackle this is with deep context analysis and massive corpus to train the natural language processing sentiment analysis model.

Neutral messages

Another issue is neutral messages, which do not classify in any category. How does the algorithm deal with neutral messages? Here are two ways to approach it:

  1. The first is to dive into context and look at all stated facts. This can bring to the forefront any unexpressed opinion. This is a manual approach for special cases.
  2. The second is algorithm-related. If something does not classify as positive or negative, the algorithm can be triggered to mark it as neutral.

The future of sentiment analysis

Sentiment analysis is valuable technology, particularly for businesses. It obtains realistic feedback from customers in an unbiased (or relatively less) biased way. When executed correctly, it adds value to an organization and provides facts and measurable data for future decision-making.

Organizations wishing to make their product or service better, drive more sales, and out-smart their competitors should utilize sentiment analysis.

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