Marketing, Technology

Chatbots as Means to Creating Better Sentiment Analysis

For good sentiment analysis, you need data and tons of it. What better way to gather this data than through live-chatting up your customers and what better means than chatbots. Anybody into data analytics will want to tell you that social media has made the gathering of data relatively easy. However, the data you gather from this source can be misleading if you want to use it for sentiment analysis. Compared to decades ago when the only ways to gather insight on what customers wanted and how they felt about a brand were by knocking on their front door and asking, cold-calling them on their wall phone or sending them a survey with a self-addressed stamped envelope, the social media has brought about a revolution and deserves accolades for that. From any angle, you look at it, the job of gathering data before the advent of social media was labor intensive but the methods employed yielded accurate information. We can’t possibly go back to those days taking into consideration, the global population and technological advancements we can reap from. Taking social media monitoring (SMM) into account, it’s easy for you to conclude that today’s ready access to customer sentiment would…

Continue Reading

Marketing, Technology

3 Ways Sentiment Analysis Improve Your Marketing Scope

Quite often, you have come across situations where you really need to understand the intent behind a message and you’re completely lost. That is the time you need sentiment analysis to come into play. Sentiment analysis which is also referred to as opinion mining is an approach to natural language processing (NLP) that identifies the positive, negative, or neutral tone behind a body of a text. It entails the use of data mining, machine learning (ML), and artificial intelligence (AI) to mine text for emotions and subjective information. Sentiment analysis systems help brands to gather insights from unorganized, confusing, and unstructured texts that come from online sources such as emails, blog posts, support tickets, web chats, social media channels, forums, and comments. Mathematical calculations replace manual data processing by implementing rule-based, automatic or hybrid methods. Rule-based systems carry out sentiment analysis based on predefined, labor-intensive means while automatic systems learn from data with machine learning techniques. A hybrid sentiment analysis combines rule-based and automatic systems. Different types of sentiment analysis Fine-grained sentiment analysis: provides a more clear-cut level of segmentation by breaking it down into further categories, most often, however, this is broken down into basically very positive or very…

Continue Reading