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An Ultimate Guide to Sentiment Analysis

Sentiment Analysis guide
Published on Sep 03, 2022

What are your customer's thoughts on products or services? This is a question that every business needs to find answers to on a continual basis. The reviews that customers express about products/services are not mere words but a powerful communication that can either build a business or break it. 

Customers' opinions about products and businesses have been increasingly visible since the emergence of social media and digital marketing. Online user feedback, such as reviews, social media comments, polls, surveys, and ratings, includes a wealth of information that cannot be ignored. This data reveals what consumers think of your product, what they like and hate about it, and, most crucially, how you can improve your product offering based on input derived from under the layers of feedback. Sentiment analysis may throw further light on these subjects and serve as a useful tool for analyzing your clients' moods and attitudes.

Sentiment analysis is growing more and more popular as technologies advance. In this regard, let's answer a few basic questions about what sentiment analysis is and how it can impact your business’ outlook.

What is Sentiment Analysis?

Sentiment Analysis, also called Opinion Mining, analyzes and monitors languages and comments that quantify attitudes, views, and feelings about a business, product, or service. It categorizes pieces of writing or comments under labels such as confrontational, moderate, or optimistic. 

Opinion Mining

Types of Sentiment Analysis?

People experience a wide spectrum of emotions – happy or sad, interested or indifferent, positive or negative. There are various algorithms available to capture this wide range of emotions.
Here are the most common forms of sentiment analysis:

  • Fine-Grained

This form of sentiment analysis segregates comments into five categories – extremely positive, positive, neutral, negative, or very negative, thus providing an exact amount of polarity. Fine-grained sentiment analysis is most useful for studying ratings and reviews.

  • Aspect-based

In evaluating the overall polarity of your customer reviews, the aspect-based analysis goes beyond fine-grained analysis. It helps you figure out which parts of the dialogue are being discussed. For example, a customer may write a review on a product claiming that the battery life is inadequate. The algorithm will then respond that the nasty feedback is about the battery life, not the product.

  • Emotion Detection

Emotion Detection identifies emotions rather than positivity and negativity. Examples of emotions are anger, sorrow, happiness, frustration, fear, worry, and panic. Lexicons – collections of words that express distinct emotions – are widely used in emotion detection systems. Certain sophisticated classifiers also use robust Machine Learning (ML) techniques.

  • Intent Analysis

Companies may save time, money, and effort by accurately detecting consumer intent. Businesses frequently find themselves pursuing customers who have no intention of purchasing soon. Accurate intent analysis can help solve this problem. The intent analysis can also assist in figuring out whether a customer intends to make a sure-shot purchase or is just window shopping.

How to conduct Sentiment Analysis?

As we dig further into understanding this powerful marketing and branding tool, let’s look at the steps applied in sentiment analysis.

conduct Sentiment Analysis

Step 1: Data Gathering

First and foremost, we require the data to be analyzed.  Scraping tools, APIs, customers' data feeds, and other methods can collect data from social media.  

Step 2: Text Cleaning

By deleting stopwords (a, and, or, but, how, what), punctuation (commas, periods), and testing for stemming, text cleaning tools will allow us to process the data and prepare it for analysis. In addition, we will be able to ‘clean’ or ‘strip’ the texts of everything that isn't important to the analysis using these methods.

Step 3: Data Processing

After cleaning the data, the next step is to process the data. The processing of data depends on the kind of information it has  text, image, video, or audio. It includes audio transcription, caption overlay, image overlay, logo recognition, and text extraction.

Step 4: Data Analysis

There are several subtasks to do in this stage of the sentiment analysis process. It includes training the model, multilingual data, custom tags, topic classification, and sentiment analysis.

Step 5: Data Visualization

The insights from data analyses are immediately translated into actionable reports in the form of graphs and charts when all the phases in the sentiment analysis process have been completed. These visual reports are extremely significant since they allow you to examine detailed, aspect-based outcomes. When you obtain an average score for your brand, for example, you can use the sentiment analysis dashboard to filter the findings to discover which features received high scores and which received poor scores. This will help you determine which regions require more attention than others.

How is Sentiment Analysis useful?

Sentiment Analysis is a technique to determine the emotional tone of texts. For example, it may determine whether a piece of writing has pleasant, fair, or unpleasant feelings.
It may help marketers to better comprehend client feedback and adapt their strategy as a result. It may also be used to analyze whether a specific campaign or product has a favorable or unfavorable impact on customers.

Market Research and Analysis:

Sentiment Analysis is a business intelligence approach used to identify the subjective reasons customers react a certain way toward something. 

Why do they buy a product? 
What do they think about the user interface? 
Does the product fulfill all their expectations and needs?

Likewise, it answers questions such as are listed above and helps examine opinions, trends, biases, reactions, and more in the fields of psychology and sociology.

Customer Service:

Sentiment or intent analysis is frequently used by customer service personnel to automate categorizing of incoming user emails into ‘urgent’ or ‘not urgent’ categories after gauging the email's sentiment, proactively detecting unhappy users. ML allows professionals to run such automation to help unravel emotions and intent.

Brand Monitoring:

One of the most well-known applications of sentiment analysis is to obtain a complete 360-degree perspective of how consumers and stakeholders perceive your brand, product, or organization. Widely available media, such as social media and product reviews across the web, provide insights into how well (or not) the product/organization is doing. Sentiment analysis also helps assess the impact of a new product, marketing campaign, or consumer's reaction to recent corporate news on social media.

Where does sentiment analysis fail and how to overcome it?

sentiment analysis fail

Even individuals face trouble interpreting their emotions effectively, rendering sentiment analysis among the most challenging tasks of Natural Language Processing (NLP).

Despite the improvements, data scientists still have a long way to go in creating more effective sentiment classifiers. Some primary issues that machine-based sentiment analyses face:

  • Sarcasm Detection: 

In sarcastic writing, people communicate their bad feelings with good phrases. 
For example: "This laptop has an awesome battery backup of 2 hours."
Rule-based, statistical, ML algorithms, and deep learning are different approaches for automatic sarcasm detection. 

  • Negation Detection:

Negation is a method of reversing the polarity of words, phrases, and even sentences. Researchers utilize a variety of linguistic principles to establish whether negation is taking place, but it is also vital to figure out what words are affected by negation terms.
For example, in the sentence “The movie was not interesting,” the scope is only the next word after the negation word. But for sentences like “I do not call this film a romantic movie,” the effect of the negation word “not” is until the end of the sentence.
The quality of a dataset for training and testing sentiment classification models inside negation will improve if samples with different types of reported negations are included.

  • Word Ambiguity:

The difficulties of defining polarity in advance is an issue with word ambiguity since the polarity of some words is heavily reliant on the context of the phrase.
For example: 

  1. “The storyline is unpredictable.”
  2. “The driving wheel is unpredictable.”

These two instances demonstrate how ‘the word emotion’ is influenced by context. The polarity of the word ‘unpredictable’ is anticipated to be positive in the first scenario. The polarity of the same word is negative in the second.

  • Multipolarity:

Multipolarity can occur in a phrase, a document, or any other unit of text that we want to examine. In many circumstances, relying only on the study’s overall outcome can be deceiving, similar to how an average might conceal important information about all the statistics that went into it.
For example: “The display colors of my new tablet are so cool, but the audio quality is not that great.”
Some sentiment analysis methods will give this statement a negative or neutral valence. In such cases, a sentiment analysis model must assign a polarity to each part of the statement; in this case, ‘display’ has a positive polarity aspect, whereas ‘audio’ has a distinctly negative polarity aspect.

In a nutshell

Sentiment analysis may be applied in various business contexts, such as brand monitoring, product analytics, customer service, and market research. Leading businesses can work quicker, more accurately, and toward more valuable purposes by embedding sentiment analysis tools into their existing data systems.

Resultantly, decision-makers can receive fresh insights, gain a deeper understanding of customers, and more effectively empower employees to meet their targets.