Big Data & Sentiment Analysis: The Dream Team!

We consume data in every possible way. That’s a fact, and here’s another one- there’s no turning back. 

Whatever we do- from shopping to swiping, blogging to commenting, sharing to subscribing, we leave a trail behind. Now, this trail of information (Big Data!) has revolutionized how businesses make predictive choices. 

Sentiment Analysis takes it a notch up by making sense of all the ‘noise’.  

     Image by Vector4stock

What Is Sentiment Analysis, Anyway? 

Conversations are not mere verbal exchanges but much more. It is interpreting what is 'being said' than mere listening. And this is exactly what Sentiment Analysis seeks to highlight- decoding what people say versus what they mean. 

Sentiment Analysis, sometimes also referred to as Opinion Mining, is a Machine Learning- Natural Language Processing (NLP) task centred around deducing subjective qualities (such as emotions, opinions, attitudes, tone, sarcasm, etc.) from the text. 

In essence, it determines whether opinions/emotions/attitudes/perceptions about a product, business, or brand are positive, negative, or neutral. 

Sentiment analysis can help you understand your audience better. This technology collects, monitors, and analyses opinions about products and business services via online mentions (blogs, comments, discussions, feedback, etc.) across channels in real time.

But, Sentiment Analysis is far from hitting the bull’s eye in offering accurate insights all the time. 

You ask why? 

Well, it took approximately 2 million years to evolve human speech. Even the machine can’t beat that.

Another important thing is the absence of emotions, which makes Sentiment Analysis one of the most challenging areas of AI. Grammar, the figures of speech, accents, slang, and the overall complexities of human language make sentiment interpretation a daunting task.  

Subjectivity is yet another obstacle. For instance, the word- “cool”. It means moderately cold- what a cool evening. But it is also used as slang to compliment or describe something as awesome or good- that outfit is so cool. Alternatively, “cool” can also be used casually to show approval- okay, cool. To understand and interpret all the sentiments and varying usage- the machine needs to learn context and intentions. 

The irony, humour, sarcasm, scepticism– there’s a lot of learning left for the machine. After all, they aren’t known for their wits!

When Sentiment Analysis is so flawed, why do we even need it? 

With all its flaws and challenges, Sentiment Analysis has still managed to achieve significant implications in the modern technology sphere–Social Media is the best example. 

Customer satisfaction is undoubtedly the foundational essence of any thriving business in 2023. With digitalization and growing competition, customer opinions have become more powerful. In such a scenario, Social Media Sentiment Analysis is a boon. It can help you enormously with efficient targeting and better brand management. 

It tells you– what customers like/dislike- love/hate about your service, products, or brand.  

It filters out the noise and helps you hear customer voices (which sometimes might get lost because not all customers are active commenters, but now listening has become much easier!). This sentiment analysis feature offers a balanced and holistic analysis of your audience's opinions/sentiments concerning your business and products in real-time.  

Image from Freepik

Marketing Evolution

If you look at the shift in marketing, you will notice that gone are the days when marketers could manipulate how a product was perceived. Now, it is more about what the customers or audience want. 

Audience opinions have become pivotal. That’s where the dream team- Big data and Sentiment Analysis make a mark!

This duo can help provide unmatched and personalised brand experiences resulting in increased revenue, an engaged customer base, reduced costs, minimised risks, keeping a tab on competitors, and seizing better business opportunities.


Big Data and Social Media are no longer mere luxuries. This team is becoming a foundational necessity for making informed predictions or decisions. All because of the ever-increasing Social Media usage resulting in massive data generation. 

Website rankings, social media monitoring, online traffic analytics, etc., are a few Big Data and Social Media metrics becoming a hit amongst companies worldwide. The catch here is- the real-time feedback. It is the ultimate wealth of any business.

But Big Data and Sentiment Analysis cannot guarantee success unless you are willing to listen!

What are your thoughts? Tell us in the comments below! 










What Makes Speech Recognition Challenging?

Speech Recognition and Its Dilemmas!


                                        Image by vectorjuice on Freepik

Can language be mastered with algorithms? Ah, the stark irony in this sentence is highly debatable; making the answer to this question less significant. 

Linguistics marrying mathematics is a complex affair not just because of the different dynamics of these fields but also due to their core foundational aspects. Go back to the school days when some of us excelled in languages but struggled with numbers or vice versa. Not many of us find a perfect balance between these two fields (or abilities). 

However, Artificial Intelligence (AI) aims to achieve that balance. It strives to make human-machine interactions smoother by decoding and identifying spoken languages and converting them into text. 

The recent advancements backed by AI and ML around integrating grammar, structure, accents, syntax, dialects, and adaptability have streamlined the pace and efficacy of human-computer interaction (HCI). It has revolutionized the overall modern communication experiences.

What’s and Whys About Speech Recognition

Automatic Speech Recognition (ASR) or computer-aided speech recognition is a machine’s ability to convert human speech into a written format, hence the name- Speech-to-Text. However, it is quite often confused with voice recognition. 

Voice recognition technology majorly focuses on identifying individual user voices using biometric technology.

Think of Speech Recognition as the initial trigger enabling voice technology to perform smoothly. We owe it to ASR technology for the quick, fun, and adaptive responses of Alexa, Cortana, or Siri (our beloved voice assistants!). Had it not been for speech recognition and its advancements, our speech would have been limited to just the audio recordings in our computers even today. 

Now, let’s take a glance at how Speech Recognition functions: analyzing the audio, breaking it into smaller parts, converting it into a machine-friendly (readable) structure, and finally using algorithms to interpret it for producing the aptest text presentation. This technology is assessed on its accuracy rate viz; speed and Word Error Rate or WER. Factors such as accent, volume, pronunciation, background noise, industry-specific jargon, etc., directly affect the WER. 

A few speech recognition algorithms and computation techniques:  

  • Natural Language Processing (NLP), 
  • Hidden Markov models (HMM), 
  • Neural networks, 
  • N-grams, 
  • Speaker Diarization (SD)

ASR has become a highly innovative and speculative field generating metadata across sources. As per Gartner’s predictions- 25% of employee interactions with various applications will be mainly via voice by 2023. A few main reasons behind its scaling popularity are: 

  • High Speed 
  • Predictive outcomes (or analytics) it can deliver
  • Its role in accelerating automation 
  • Its ability to cater exceptionally well to the rapidly growing “remote world”
  • Coost-effective- it just requires the initial investment rather than recurrent costs using manual methods. 

Why is Speech Recognition hard?

Our language is arbitrary. Hence, its peculiarities and complexities make it very challenging for the machine to analyze and produce error-free transcription. Further, the involvement of various abbreviations, syntaxes, acronyms, phrases, dialects, accents, context, semantics, pragmatics, pauses, etc., poses dilemmas limiting ASR’s efficacy, efficiency, and accuracy.  

The biggest speech recognition challenges:

1) Imprecision and Misinterpretations: Context is key. 

To master this, the machine would have to learn, but most importantly, understand the difference between- hearing and listening. While communicating, we take into account the speaker’s expressions, body language, tone, pitch and then determine the meaning (as well as sentiments behind it). 

But for the machine, it is in a tough spot, since it lacks contextual experience (and sentiments) and runs solely on algorithms. 

2) Background noise: hinders accuracy big time. 

Loud surroundings and background noises make it unfit and unreliable for outdoor or large public spaces. The technology lags in mitigating and filtering background noises to isolate the human voice. Hence, additional external devices (like headsets) can help in this scenario, but that is just too much extra baggage. Another aid here is acoustic training, which comes with its limitations too.   

3) Language Base: The more, the merrier!

The current gap in language coverage provides a barrier to adoption. The huge number of varying accents and dialects are amongst the biggest factors impacting accuracy. That’s why we not only need more languages in the arena but equally more accents, and dialects. This can help in providing more exposure, experience, and better learning opportunities to the machine. 

4) Data security and privacy: cost and implementation

For a machine to learn and train, massive data input is required. The current approach to obtaining data via paid research or studies is very restrictive. It forms a fraction of the total voice data generated in this digital age. Accessing, using, and managing the collected data raises questions about data security and individual user privacy. 

This conflict of interest narrows the availability of data inputs required for AI, making data accessibility even harder. 



Speech recognition technology is an inclusive package deal a user gets with any modern digital experience (that’s how embedded it has become). This evolving technology revolves around adaptability, paving the way for more unique use cases.

Human’s quest to streamline human-machine interaction has come a long way. Sure, it ain’t perfect at the moment- nothing is- but who knows what the future holds!