Exploring Social Media Sentiment Analysis

The Appeal

The world has changed considerably since the advent of the internet. Today, we’re all residents of a global village. The interconnectedness that exists today means everyone’s in proximity. However, since everyone exists in a digital plane, there’s little emotional data available. With little to no emotional data available, it becomes hard for businesses to determine people’s emotions and interests. That’s where social media sentiment analysis comes in.  Social media sentiment analysis is the process of retrieving information about a consumer’s perception of a product, service, or brand. Previously, businesses used feedback surveys to obtain insights into their products. However, this process had all sorts of problems; too many people were not bothering to fill them out. Even if they did, its validity and reliability were always in question. Hence, the data available to them was scarce and unreliable.  However, with social media emotion analysis, businesses uncovered a valuable understanding of consumer preference. This allows them to alter their operations accordingly, thus maximizing their profit margins. However, how is social sentiment measured? Let’s take a closer look. Methodology The process of quantifying emotion may seem absurd to many. The question that most commonly arises is, “How do you analyze sentiment?”.  As responses are subjective, turning them into quantitative data requires effort. However, the methodology being implemented measures the frequency and reception of your business online.  By monitoring sites to check your firm’s mentions, you obtain a clearer picture of the reactions you receive. Then, said reactions to your business are examined. Thus, providing insights into how people view your products and services.  Since the world has become increasingly digitized, the number of online users has increased. And it continues to keep on expanding. This makes manual analysis almost impossible and inefficient. Browsing every mention on multiple sites and recording them takes effort and time. That’s why people are turning towards automated analysis. This streamlines the whole process, making it considerably more efficient and lucrative. The most popular method to conduct this analysis is automating the whole process. To that end, Machine Learning and Natural Language Processing (NLP) are best suited. They analyze and classify emotions expressed online into different categories. They achieve this by evaluating the various posts and comments about your business online. This yields different types of sentiment analysis. Then, it categorizes each mention into positive, neutral, or negative. Their self-learning disposition allows them to improve their accuracy with time, making it more efficient.

Applications 

• Brand Reputation Management: Arguably, the most important feature of social media sentiment analysis is monitoring brand reputation. If not addressed promptly and effectively, bad reviews can damage a company. One negative review can snowball into a giant problem. Moreover, you can track your firm’s image over time, observing whether it has improved or not. This allows you to take steps to better your public image accordingly.

• Customer Feedback Analysis: Keeping track of each mention of your firm across platforms may take too much work. Furthermore, analyzing the content of each review remains a task unto itself. That’s why automating the process and combining all the data makes evaluation easy. Moreover, categorizing data into different patterns allows you to observe different trends. Thus allowing firms to alter their policies and performance for maximum efficiency. 

• Trend Analysis: Another important feature of this method is the knowledge it provides. It allows businesses to stay in tune with the trends and culture growing online. This enables them to tap into that desire by marketing their products in that light. Furthermore, it helps them keep up with the competition by keeping in touch with consumer preferences. That’s why they can avoid becoming obsolete by always satisfying customer requirements.

Tools & Techniques

As with each methodology that exists today, there’s more than one way to go about it. Today, various social media sentiment analysis tools exist to help you monitor your perception.  Each tool and technique offers something unique and special. Thus enabling you to achieve the same end through different means. Below are listed a few popular tools for you to consider.

• Hootsuite Insights
• Talkwalker
• Meltwater
• Idiomatic
• MonkeyLearn
• Brandwatch

However, in each situation where multiple options are present, which do you choose?  Well, there’s no one right answer to it. Different businesses enjoy different dynamics. Hence, the tools that suit may not be fit for the other. Therefore, different considerations have to be evaluated before making a decision. The most crucial aspect to consider during the process is the nature of your business. The demographic, country, ethnicity, region, sex, etc., your product appeals towards. This allows you to opt for the tool that checks all your required boxes.

Challenges 

Like with each novel advent, this process was bound to run into problems. Couple that with the vibrant and diverse population on the internet, and the prospect seemed grim.

• Data Quality: One of the challenges social media sentiment analysis faced was the quality of data available. Since the space can be best described as an informal, noisy, and vocal one, quantifying poses its challenges. That’s why determining the quality of data before examining it becomes a challenge. This may result in the algorithm showing bias towards a specific set of data. Consequently, it muddles your analysis and, ultimately, your performance.

• Accuracy: Since language is a rich tapestry, analyzing it isn’t easy. The duality of words, negation, structuring, etc., makes it challenging to extract meaningful insights. Furthermore, the difference in regions results in language being used differently. This poses a threat to the accuracy of the evaluation of the algorithm. As content becomes difficult to examine, the accuracy of the results falters. 

• Cultural Nuance: Although the internet has become a global village, there’s still a distinct difference. People from different parts of the world enjoy their unique culture. This results in a plural set of human behavior. This makes it increasingly difficult to monitor emotions. Whether it’s detecting sarcasm, slang, or expression itself, human emotion is complex. That’s why an algorithm needs to encompass all these aspects in order to provide valuable details.

Future Direction

With the advent of AI, machine learning, and deep learning advancing rapidly, the future looks bright. The social media sentiment analysis will only advance with time. Complex AI-driven algorithms will be able to detect a wide range of human emotions. Thus, they’ll become able to quantify an array of human emotions. Furthermore, the swift growth in the AI industry points towards its growing influence in society, particularly in the automation of various tasks. This will enable this examination to take place on a wide scale. Unearthing an abundance of quality data efficiently and regularly.

Conclusion

All in all, social media sentiment analysis has become a pivotal tool for businesses today. Its ability to provide novel insights into consumer preference remains crucial. Furthermore, its importance with time is only going to increase. Making it an essential part of marketing strategies in the future.