The presence of social media today has made it possible for people to stay connected all the time. Be it something as simple as a status about the happenings of the day, a film review, or debates about various socio-political scenarios; the masses are increasingly expressing their opinions on multiple platforms, much like the conversations that happen offline. In fact, these online conversations have become more prominent than offline ones, since they gather a much higher reach in very little time. This is nothing but massive amounts of raw data waiting to be analyzed, and this data has given rise to a new science in recent years: the science of opinion mining.
Monitoring what people are saying online through their likes, dislikes and comments is called sentiment analysis. This helps us figure out how people feel, whereas opinion mining goes one step ahead to analyze why they feel the way they do. Backed by machine learning, opinion mining has gained massive momentum in today’s time. This has great potential as it can extract valuable insights from social data and be hugely beneficial for brands. They have realized its importance because these conversations are a direct connection to the consumer, and a goldmine in terms of their reactions and emotions, that too, in real time.
Shifts in social media sentiment have been known to correlate with stock market shifts directly. The power to read through and make sense of public opinion also helps brands chart out targeted marketing strategies. Not only can you know what people think of your product, but you can also look up what your competitors are doing and plan accordingly. For example, an online user may comment on how the new car he purchased is real value for money, but he is not too happy with the interiors. Once analyzed, this data can help market research and in turn, lead to the better overall customer experience. This is the future of marketing.
Pretty interesting, right? It’s even more interesting to know how this works. Opinion mining uses Natural Language Processing to identify a range of opinions about topics in a given pool of text. Artificial intelligence is used to break down content into component parts and understanding the true nature of the feelings being communicated. These opinions are scored positively to negatively using certain words that aid sentiment analysis and determine trends in attitude and mood. For example, words like ‘love,’ ‘excellent,’ ‘happy’ and ‘enjoy’ are ranked highly positive, while words like ‘dislike’ and ‘unhappy’ are considered negative. This process, however, is yet to be perfected.
The human language itself is continually evolving, which is why the process cannot be left to AI and technology alone. Emotional intensity, sophistication and style, unintentional ambiguity, sarcasm, colloquialisms, and the fact that people express opinions differently are the current challenges that govern the accuracy of opinion mining. People can also be contradictory in their statements, and this is something that algorithms have not been able to catch yet. Without human intervention, the process remains flawed. Policy makers, media houses, international businesses, and consumer product manufacturers stand to gain a great deal by implementing opinion mining, and the sooner they realize this, the better chances of success they will have!