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Sentiment Analysis: Why Twitter Is A Buzzing Goldmine for Marketers?

Sentiment Analysis: Why Twitter Is A Buzzing Goldmine for Marketers?

Sentiment analysis has become a buzzword lately as social networks are bustling with consumer chatter. Sentiment analysis, also known as opinion mining, is the application of Natural Language Processing (NLP) techniques and text analytics for identifying patterns and extracting insights from consumer data. It is widely used by brands to gauge the reactions of their consumers towards their products, product features, promotional campaigns and so on.

Twitter Sentiment Analysis

From companies using sentiment data for monitoring consumer perception, to public figures and celebrities leveraging the power of sentiment data for impression management; sentiment data finds its use cases in the unlikeliest of places. Here are some of the popular use cases of sentiment analysis that we at PromptCloud witness frequently:

Social CRM

Traditionally, companies seeking feedback about their products and services used to send questionnaires and surveys to their users, or had to host some focused group discussions to uncover deeper insights about their marketing efforts.

Internet Reputation on What are they saying about?

The problem with this approach is that the sample size is rather limited to a handful of people, which might not always be reflective of the entire population set, and a number of biases can affect the outcome of research.Sentiment analysis has solved this problem by automating the process of extracting consumer chatter from social media networks, forums and blogs/news sites. Companies can now achieve more accurate sentiment classification and insights about their products, specific features and customers’ overall experience with the brand.

Recommendations

A lot of people who are looking for others’ experiences, ask for specific inputs about the products or services in their consideration set over social media. Most users are openly expressing their likes and dislikes about certain products and even giving their feedback on details such as product features, promotion campaigns, customer service, delivery times (for e-commerce) and overall experience with the company.

Reputation Management

Brand Reputation in Social Media

Apart from listening to customers, companies are also increasingly becoming concerned about their overall brand perception online. Towards that end, they’ve started monitoring any negative feedback and have set up online grievance redressal systems. Nowadays, companies are more receptive to customer feedback and are quickly resolving any complaints through special reputation management teams. The various channels where companies have become active are social media networks, forums, special community sites such as Dell and Apple, reviews on e-commerce sites and consumer complaint sites such as BBB.

Lead Generation

An uncommon use case has emerged out of social media data, as many companies have started pursuing their clients over Twitter/LinkedIn for business opportunities. For instance, if a user is looking for a particular service provider in their city, this is a great opportunity for local dry cleaners to pitch their services to them. Similarly,  proactive sales people are connecting with prospective customers who’re interested in related services. In order to monitor such tweets and catch them before your competitors do, it’s important to have continuous access to such tweets – something that we provide as an offering to our clients.

Personal Lead Generation tweets

Why Twitter Data is A Goldmine for Marketers

Microblogging platforms such as Twitter have become quite popular in the last few years. It’s really surprising that even though restricted to communicate in just 160 characters or less, people are able to express so much more. Once companies started mining Twitter data, a new feedback channel opened up which didn’t exist earlier – one that greatly represents and affects popular opinion and does so in the real time.

A major challenge involving Twitter sentiment analysis is the major breadth of topics that are covered. People talk about anything and everything, which makes Twitter always bursting with data. Thus it becomes important to sift through the chaff and narrow down to relevant categories based on keywords and #hashtags. Once data is obtained, the next step is to normalize and validate it against predefined parameters. Through text analytics, the sliced and diced data is classified into positive, negative and neutral statements. Some companies also add extremely-positive and extremely-negative parameters to this classification.

Twitter Firehose

Ranked no. 8 in global traffic ranking by Alexa, Twitter boasts of millions of active users posting throughout the day. Twitter provides API access to those who’re interested in extracting data from the site but that’s not comprehensive. Companies can mine Twitter data either using its Search API or Streaming API. The basic difference these two APIs is that in Search API, the user can specify a search criteria and the output would include tweets that have already been posted; while through Streaming API, users get a push of tweets happening in real-time. In the Streaming API, users input certain criteria such as keywords, usernames, demographics,

Firehose

Twitter Firehose, on the other hand, is a way to access 100% of all the tweets that are posted on the site. The social network has given access to two companies, DataSift and GNIP to act as vendors of this firehose. Other companies looking to mine data through the Firehose usually contact these two vendors with a search criteria (just like in case of the streaming API) and receive the complete data.

Access to Twitter data can be costly and difficult to manage just because of the quantum of data involved. At PromptCloud, we solve that exact problem through our near real time Twitter Crawling Service, especially useful when you are looking to extract focused data that match your criteria and are much lower in volumes that the entire Twitter firehose.

 Image Credits : datumbox

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