Sentiment analysis, which studies social media users' emotions through their posts and comments, is part of social media analytics.
Sentiment analysis is quite similar to your car's steering wheel because it determines the right direction for your social media marketing strategy based on how your target audience truly feels about your brand and what they really want.
Sentiment analysis of social media data is not as simple as you might think. Developing an algorithm to interpret human emotions, especially without body gestures or facial expressions, takes engineers a long time and considerable amount of effort to study and improve.
Some might say that sentiment analysis is a crazy scam since humans' feelings are complicated and impossible to exactly predict, especially through computer screens and texts.
As for me, I believe there is a solution to this and we still have a chance to experience and understand how humans are actually really feeling behind those texts, images and video posts. And there is a way to do that accurately, even though most sentiment analysis as part of social media analytics tools is totally fu**ed up.
There are 3 ways of doing sentiment analysis.
1. Human Interpretation
When social media was born, the concept of ‘social analytics’ or ‘sentiment analysis’ didn’t exist yet, but we have always been trying to understand others’ feelings via comments and posts to communicate better. Companies started to listen to their target audience in social media so to convey their message more effectively. These companies initially took the approach of hiring human analysts to analyze conversations in social media. This approach has been known as the “human interpretation”, mainly relying on human intelligence. Basically, each posts and comments were analyzed separately and manually by humans.
The method is as old as my grandmother but it produces the most accurate interpretation of more complicated posts such as photos, videos, gifs or sarcasm.
However, if you use a human to crack social media data, you have to put in huge, and I mean HUGE amount of time and resources into it. Also, the amount of data that can be analyzed is limited.
Human intelligence is the best option for start-up businesses because they are still small and their brand is not known yet, so the amount of data to analyze is not that big. If your company is small or medium-sized, you could consider using only human intelligence by hiring your own data analysts to interpret all your social media data accurately. But for bigger enterprises, this method may be an impossible one to pursue.
2. Keyword Tracking
As the name implies, sentiment analysis can be done by tracking relevant keywords in social media conversations. For example, a sports gear manufacturer XSports can track keywords such as “XSports", “Xmountbike", #XSports or #Xmountbike in social posts. Typical and simplest form of social media analytics tool can track keywords and interpret the posts with a predetermined method to understand the sentiment in social media around the XSports brand; for example, positive words can be “good", “great" or “amazing" and negative words can be “bad", “disappointing", etc.
This method is simple and fast and it is much affordable than using human intelligence when tracking multiple keywords and topics.
However, the result of the analysis is only 50-60% accurate because the algorithm is built to look for words and match them with predetermined meanings regardless of context, sarcastic element, or ambiguousness, especially when there are images and videos combined with texts in social posts. If your sentiment analysis is only 50-60% accurate, you might formulate your social media marketing strategy based on incorrect information, leading to detrimental result. This should not be acceptable for marketing professionals.
3. NLP (Natural Language Processing)
Sentiment analysis that is based on NLP algorithms is usually costly because it requires in-depth research and long-term development. NLP (Natural Language Processing) is an interdisciplinary study combining computer science, artificial intelligence (including tasks performed by computers) and computational linguistics (processing natural languages based on computational point of view).
Computer scientists and natural linguistic experts work together in a team to create NLP algorithm based on the linguistic elements such as grammar, phrases, idioms, collocation, etc. One unique characteristic of NLP algorithm is that it can interpret slang words or jargons like "OMG", "Vlogger", "Youtuber", "wallflower" (someone who never shows themselves in social media) or "no chill" (describing an irrational action) in order to avoid misunderstanding meanings of natural words.
Even though NLP is an advanced algorithm, computer technology still lacks human's intelligence, so there is still a big risk that posts containing images and videos would be misinterpreted.
The Solution: Combining NLP algorithm and human intelligence
As mentioned earlier, since NLP algorithm only focuses on analyzing linguistic elements to interpret texts accurately, it might make mistakes if there are additional elements such as videos and photos which is used to clarify the context that the text is expressing. And using only human analysts would be way too expensive for bigger enterprises trying to analyze massive amount of data. So, what if we combine human analysts with NLP algorithms? They would be a perfect couple because human analysts can verify some of the more complicated posts that are combining sarcasms with image posts. On top of this, we add artificial intelligence that learns from all the past data, which would create an efficient and accurate system to analyze social sentiment.
Whatever social media analytics tool you might be using now, the solution we have come up with is an entirely new approach, which is really worth seeing for yourself. It never hurts to try. Contact us to see a demo!