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Understanding Sentiment Analysis

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Understanding Sentiment Analysis

A simple chatbot is not enough to enhance your clients’ experience and boost your business performance. Sentiment analysis might be your missing piece.

· AI Zone ·
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A simple chatbot is not enough to talk to enhance your clients’ experience and boost your business performance. When it comes to development, you realize that you require sentiment analysis to understand their feedback.

It might not seem a particularly difficult task to a human, but discerning intent behind each comment requires significant effort and specific algorithms. Our texts, blog posts, and any other form of written communication is riddled with emotion. An AI system may have little trouble understanding what a short and straightforward “I like it” means, but, in most cases, a single message contains more layers of context, subtext, irony, and other distorting factors. To classify each message correctly and respond appropriately, AI-powered communication systems rely on sentiment analysis.

While simple sentiment analysis systems can be built by freelancers or individual programmers, you’ll require the help of a seasoned vendor with substantial expertise in the niche for the development of quality AI solutions. However, before getting down to the whole process, it is essential to understand what sentiment analysis is and how it might benefit your business. 

What Is Sentiment Analysis?

Sentiment analysis combines machine learning and text analytics to determine whether the intent behind a certain piece of text is positive, negative, or neutral. Businesses can apply it to gain insights into their customers’ experience with their own and competitors’ brands. The technology also powers automated communication, allowing chatbots to handle conversations more easily and efficiently.

In more broad terms, companies frequently use sentiment analysis for wide-scope research. For instance, this tool is useful to analyze the mentions of your products or brand on social media platforms. Sentiment analysis helps monitor the company’s online image and reputation, as well as to conduct market research more comprehensively. After gaining these insights, you can feed them into another model and receive predictions on how you should adjust your activity to improve customer satisfaction and engagement.

What Types of Sentiment Analysis Are There?

Sentiment analysis is an umbrella term for the technologies that strive to identify the emotion behind a user’s message. Each company can and should tailor it to its specific needs to gain the best results. Below are three of the most popular variations of sentiment analysis techniques that are widely used and generally sufficient for the majority of tasks.

Fine-Grained Sentiment Analysis

“Positive,” “negative,” and “neutral” classifications of the visitor’s intent are called polarity categories. Having three polarity categories is sufficient to interpret the basic sentiment of each message relatively accurately, but sometimes you need to go deeper.

Fine-grained sentiment analysis expands the number of polarity categories to give you a better perspective on people’s attitudes. Usually, this type of analysis includes five categories:

  • Very positive
  • Positive
  • Neutral
  • Negative
  • Very negative

Besides recognizing sentiment in written text, this technique is useful to process ratings that users give your company since you can adjust the model to the rating scale the company uses.

Aspect-Based Sentiment Analysis

This type of sentiment analysis is useful when you need to focus on what exactly your customers liked or disliked about your product. This sort of examination goes deeper and takes into consideration separate elements in the text, attaching sentiment to each of them instead of merely evaluating the whole picture. For instance, if a visitor reveals in their review that they really liked your restaurant’s menu but couldn’t find a parking spot, an aspect-based analysis program will be able to distinguish between the positive feedback on the menu and the negative on the parking arrangements.

Sentiment Analysis to Identify Emotion

In some cases, learning whether a customer’s sentiment is simply positive or negative is not enough to gain valuable information that can be further applied to benefit your business. If you need to know what emotion is prevalent in a customer’s review, this type of analysis can be suitable for you. The technique uses lists of specific vocabulary along with machine learning algorithms to determine whether a person’s text expresses gratitude, anger, satisfaction, happiness, frustration, or any other emotion.

This type of analysis also demonstrates some of the challenges that the technology faces today. People use different words and expressions to convey their feelings, which frequently creates challenges to identifying the true meaning of your customers’ words. Depending on the context, for instance, the word “crying” can be both positive and negative. In the sentence “omg, I’m crying,” it most likely means that the person finds the content hilarious. At the same time, receiving something along the lines of “I’ve been crying because of your customer support” is not exactly an example of positive feedback.

The currently available sentiment analysis systems are capable of distinguishing the emotional aspect in written text fairly accurately, but it’s also important to remember about the limitations of this technology.

How to Perform Sentiment Analysis? 

Step 1: Gather the Data

The first step is to gather the required data using social media platforms’ APIs or various scraping programs. You can also use the data from user reviews on popular resources, such as Google and Yelp, or on more targeted sites depending on which demographic or location you wish to examine. Overall, you want to gather as much information about your company as possible, including all mentions as well as direct reviews, left over a specified period.

Step 2: Pre-Process the Text Sample

The information that you have gathered is too messy to be useful at this stage. For instance, punctuation and words like “a,” “and,” “but,” and “what” are redundant for your purposes, so it’s best to remove them from the text before the analysis is carried out. You can clean your text sample up using specialized tools or by writing a piece of code if you’re familiar with some programming language. Python, in particular, features extensive libraries that allow you to manipulate the text data easily.

Step 3: Use Sentiment Analysis Tools

You can build a program for sentiment analysis in Python, using the Natural Language Toolkit (NLTK). The library features data cleaning and analyzing functionalities that make it easy to create and test a model even for beginners.

However, if you have little experience coding, using one of the readily available commercial solutions is a better option, as it removes not only the necessity to build the classifier but manually incorporate it into your system. Services like Brandwatch, Critical Mention, and Repustate are incredible for tailored sentiment analysis. They also represent the varying price ranges in the field, which means you are likely to find the right fit regardless of your budget.

Step 4: Understand and Apply the Results

When the analysis is done, you will obtain several major categories, depending on how your software was programmed to categorize this information. In the simplest case, you will be able to see the number and percentage of positive, negative, and neutral comments, as well as when each identified sentiment was left. The latter can help discern the peaks when the customers were the most and the least satisfied with your company. This can help further inspect the company’s marketing strategies and overall activity during specific periods, finding what works well and what needs to be adjusted.

Wrapping Up

Sentiment analysis is a helpful tool in studying your customers’ experiences with your products and improving automated communication. This technology aims to comprehend the emotion and intent behind each message. It can also help you spend less time gathering this information by hand and enable you to create prediction models using more data.

You can perform sentiment analysis in four general steps: collect the data, clean it up, use appropriate machine learning tools, and utilize the results. If you are familiar with the general principles of computer programming and data science, you can build a sentiment analysis classifier yourself.

For non-programmers, third-party services like Brandwatch and Critical Mention exist to help you become more aware of your brand’s reputation and communicate with your customers more effectively.

Topics:
ai, artifical intelligence, sentiment analysis

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