Sentiment Analysis: Analysis, Applications & Tools

June 24, 2022

Sentiment analysis is a natural language processing (NLP) technique for determining the positivity, negativity, or neutrality of data. Sentiment analysis is frequently used on textual data to assist organizations in tracking brand and product sentiment in consumer feedback and better understanding customer demands. 

Here, we will be discussing- What sentiment analysis is? How to conduct it? Its applications? What tools can you use to do it? 

Table of Content:

  1. What is Sentiment Analysis?
  2. How to conduct sentiment analysis?
  3. Application of Sentiment Analysis:
  4. Conclusion:

What is Sentiment Analysis?

Sentiment analysis is text mining that recognizes and extracts subjective information from the source material, allowing a company to determine the social sentiment of its service, brand, and product while monitoring online conversations. In most cases, however, social media stream analysis is limited to count-based metrics and basic sentiment analysis. This is analogous to only scraping the surface and missing out on those high-value ideas that are just waiting to be found. So, what can a company do to take advantage of the low-hanging fruit?

In sentiment analysis, you may examine text at varying degrees of depth, depending on your objectives. You might, for example, use the average emotional tone of a bunch of reviews to figure out what proportion of people enjoyed your new apparel line. If you want to discover what visitors like and hate about a certain garment and why, or whether they compare it to comparable goods from other companies, you'll need to examine each review phrase for specific elements and keyword usage. Two forms of analysis can be utilized, depending on the scale: coarse-grained and fine-grained. A sentiment can be defined on a document or phrase level using coarse-grained analysis. You can also extract a sentiment in each sentence part via fine-grained analysis.

How to conduct sentiment analysis? 

Sentiment analysis methods and technologies enable you to examine your operations from the perspective of your customers. But how can you get such information out of user-generated data? 

To begin, compile all relevant brand references into a single document. Consider your selection criteria: should these references be restricted in time, utilize just one language, or originate from a specified area, for example- The data must next be prepared for analysis, which includes reading it, removing any non-textual content, correcting grammar errors or typos, and removing all irrelevant items such as information about reviewers, among other things. We can evaluate and extract sentiment from data once it has been prepared. Because dozens, if not hundreds of thousands, of mentions may need to be analyzed, the ideal approach is to use software to automate this time-consuming task. Using commercially available tools and APIs. Various customer experience software gathers input from a variety of sources, provides real-time notifications on mentions, analyzes text, and visualizes the results.

Sentiment analysis is a function of text analysis platforms and tools, and it is merged with AI software that analyses text data to help you rapidly discover how people feel about your brand, product, or service. Sentiment analysis solutions function by automatically identifying the emotion, tone, and urgency in online chats and assigning them a positive, negative, or neutral tag, allowing you to prioritize consumer inquiries. Brandwatch, Lexalytics, Social Searcher, MeaningCloud, Talkwalker, Quick Search, and Rosette are just a handful of the sentiment analysis tools accessible.

Application of Sentiment Analysis:

Customers contact organizations in a variety of ways that make it difficult for employees to remain on top of everything. However, using sentiment analysis software, you may automatically sort your data as it enters your help desk. Let's look at some of the most common sentiment analysis applications:

  1. Social media monitoring: Because they're uninvited, social media posts can contain some of the most candid thoughts on your products, services, and enterprises. You can sift through all of that data in minutes with sentiment analysis tools, analyzing individual emotions and general public sentiment on every social site. Sentiment analysis can identify sarcasm, interpret popular chat acronyms (lol, ROFL, etc. ), and rectify common errors such as misspelled and misused words beyond simple definitions.

  1. Customer support: Due to the enormous volume of requests, diversified themes, and many departments within a firm – not to mention the urgency of each particular request – customer service administration poses numerous obstacles. Sentiment analysis using natural language understanding (NLU) scans ordinary human language for meaning, emotion, tone, and more, much like a person would, to comprehend client demands. To prioritize any important concerns, you may automatically handle customer service requests, online chats, phone calls and emails by emotion.

  1. Brand monitoring and reputation management: One of the most common uses of sentiment analysis in the corporate world is brand monitoring. Bad reviews may quickly accumulate on the internet, and the longer you wait to respond, the worse the problem will get. Negative brand references will be promptly alerted to you using sentiment analysis technologies. Not only that, but you can track the image and reputation of your brand over time or at any specific point in time, allowing you to measure your success. Whether you're looking for information about your brand in news stories, blogs, forums, or social media, you can turn that data into useful data and statistics.

  1. Product analysis: Find out what people are saying about a new product soon after it is released, or go through years of comments you may not have seen before. You may utilize aspect-based sentiment analysis to locate only the information you need by searching keywords for a certain product attribute (interface, UX, functionality). Learn how your target audience perceives a product, which aspects of the product need to be enhanced, and what will make your most valued consumers happy. All of this is possible because of sentiment analysis.

  1. Market and competitor research: For market and competition research, use sentiment analysis. Find out who among your rivals is getting favorable press and how your marketing efforts stack up. Examine the positive language your rivals use to communicate with their clients and incorporate some of it into your own brand message and voice guide.

Conclusion-

With technological advancements, the age of gaining useful insights from social media data has come. Sentiment analysis enables companies to make use of vast volumes of unstructured data to better understand their customers' demands and opinions about their brand. 

Online chats are monitored by businesses in order to enhance their products and services and retain their reputation. The research elevates customer service to a new level. Customer service systems use Sentiment Analysis to categorize incoming inquiries by urgency, letting personnel prioritize the most demanding consumers. Sentiment analysis may also be used for workforce analytics.

If you have not considered using sentiment analysis for crunching your user database, then what are you waiting for?

Latest Blogs
This is a decorative image for: A Complete Guide To Customer Acquisition For Startups
October 18, 2022

A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

  • Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

  • MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
  • CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.  
  • CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
  • Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

  • Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

  • Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels. 

  • Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

Reference Links

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

This is a decorative image for: Constructing 3D objects through Deep Learning
October 18, 2022

Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

  • Highly calibrated cameras that take a photograph of the image from various angles.
  • Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

  • Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

  • Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

  • Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

  • Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

  • Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

  • 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
  • It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website

Reference Links

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

This is a decorative image for: Comprehensive Guide to Deep Q-Learning for Data Science Enthusiasts
October 18, 2022

A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
  3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
  4. Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.  

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

Reference Links

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

Build on the most powerful infrastructure cloud

A vector illustration of a tech city using latest cloud technologies & infrastructure