Humans are sentient beings; we experience emotions, sensations, and feelings 90% of the time. Sentiment evaluation is becoming increasingly necessary for researchers, businesses, and organizations to grasp customer feedback and discover areas of improvement. It has various applications, yet it faces some challenges too.

Sentiment refers to thoughts, views, and attitudes – held or expressed – motivated by emotions. As an example, most individuals today just get onto social media to precise their sentiments in content reminiscent of a tweet. Hence, text mining researchers work on social media sentiment evaluation to grasp public opinion, predict trends and improve customer experience.

Let’s discuss sentiment evaluation intimately below.

What’s Sentiment Evaluation?

Natural Language Processing (NLP) technique to investigate textual data, reminiscent of customer reviews, to grasp the emotion behind the text and classify it as positive, negative, or neutral is known as sentiment evaluation.

The quantity of textual data shared online is big. Greater than 500 million tweets are shared each day with sentiments and opinions. By developing the capability to investigate this high-volume, high-variety, and high-velocity data, organizations could make data-driven decisions.

There are three principal kinds of sentiment evaluation:

1. Multimodal Sentiment Evaluation

It’s a variety of sentiment evaluation by which we consider multiple data modes, reminiscent of video, audio, and text, to investigate the emotions expressed within the content. Considering visual and auditory cues reminiscent of facial expressions, tone of voice gives a broad spectrum of sentiments.

2. Aspect-based Sentiment Evaluation

The aspect-based evaluation involves NLP methods to investigate and extract emotions and opinions related to specific facets or features of services and products. For instance, in a restaurant review, researchers can extract sentiments related to food, service, ambiance, etc.

3. Multilingual Sentiment Evaluation

Each language has a unique grammar, syntax, and vocabulary. The sentiment is expressed otherwise in each language. In multilingual sentiment evaluation, each language is specifically trained to extract the sentiment of the text being analyzed.

What Tools Can You Use for Sentiment Evaluation?

In sentiment evaluation, we gather the information (customer reviews, social media posts, comments, etc.), preprocess it (remove unwanted text, tokenization, POS tagging, stemming/lemmatization), extract features (converting words to numbers for modeling), and classify the text as either positive, negative or neutral.

Various Python libraries and commercially available tools ease the technique of analyzing sentiment, which is as follows:

1. Python Libraries

NLTK (Natural Language Toolkit) is the widely used text processing library for sentiment evaluation. Various other libraries reminiscent of Vader (Valence Aware Dictionary and sEntiment Reasoner) and TextBlob are built on top of NLTK.

BERT (Bidirectional Encoder Representations from Transformers) is a robust language representation model that has shown state-of-the-art results on many NLP tasks.

2. Commercially Available Tools

Developers and businesses can use many commercially available tools for his or her applications. These tools are customizable, so preprocessing and modeling techniques might be tailored to specific needs. Popular tools are:

IBM Watson NLU is a cloud-based service that assists with text analytics, reminiscent of sentiment evaluation. It supports multiple languages and uses deep learning to discover sentiments.

Google’s Natural Language API can perform various NLP tasks. The API uses machine learning and pre-trained models to offer sentiment and magnitude scores.

Applications of Sentiment Evaluation

1. Customer Experience Management (CEM)

Extracting and analyzing customers’ sentiments from feedback and reviews to enhance services and products is known as customer experience management. Put simply, CEM – using sentiment evaluation – can enhance customer satisfaction which in turn increases revenue. And when customers are satisfied, 72% of them will share their experience with others.

2. Social Media Evaluation

About 65% of the world’s population uses social media. Today, we will find sentiments and opinions of individuals about any significant event. Researchers can assess public opinion by gathering data about specific events.

For instance, a study was conducted to match what views people in Western countries have about ISIS as in comparison with Eastern countries. The research concluded that individuals view ISIS as a threat no matter where they’re from.

3. Political Evaluation

By analyzing public sentiment on social media, political campaigns can understand their strengths and weaknesses and reply to the problems that matter most to the general public. Furthermore, researchers can predict election results by analyzing sentiments towards political parties and candidates.

Twitter has a 94% correlation with polling data, meaning that it is extremely consistent in predicting elections.

Challenges of Sentiment Evaluation

1. Ambiguity

Ambiguity refers to instances where a word or expression has multiple meanings based on the encircling context. For instance, the word sick can have positive connotations (“That concert was sick”) or negative connotations(“I’m sick”), depending on the context.

2. Sarcasm

Detecting sarcasm in a text might be difficult because individuals with the stimulus can use positive words to precise negative sentiments or vice versa. For instance, the text “Oh great, one other meeting” could be a sarcastic comment depending on the context.

3. Data Quality

Finding quality domain-specific data with no data privacy and security concerns might be difficult. Scrapping data from social media web sites is at all times a gray zone. Meta filed a lawsuit against two corporations BrandTotal and Unimania, for making scraping extensions for Facebook against Facebook’s terms and policies.

4. Emojis

Emojis are increasingly getting used to precise emotions in conversation on social media apps. However the interpretation of emojis is subjective and context-dependent. Most practitioners remove emojis from the text, which might not be the most effective option in some instances. Hence, it becomes difficult to investigate the sentiment of the text holistically.

State of Sentiment Evaluation in 2023 & Beyond!

Large language models like BERT and GPT have achieved state-of-the-art results on many NLP tasks. Researchers are using emoji embedding and Multi-Head Self-Attention Architecture to handle the challenge of emojis and sarcasm within the text, respectively. Over time, such techniques will achieve higher accuracy, scalability, and speed.

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