DEV Community: shreya sahani

Improve customer experience Insight with sentiment analysis.

shreya sahani — Thu, 21 Apr 2022 06:31:53 +0000

In today’s competitive world of business, customer experience is the topmost priority for all companies across every industry to thrive and survive. In order to sell more products and services, generate more profit, and ensure business growth, you need to get in sync with your customers’ needs and expectations on a consistent basis.

Surveys have always been one efficient way to gather data on CX. However, Sentiment Analysis has just made its way into being an effective approach to dive deep and understand the customer’s mind in a more intimate way.

All you need to have is to have the access to a CX insight solution that is backed by sentiment analysis as it shows you different parts of your customer’s engagement and strategies as to which ones are delivering smooth customer experience insights and which ones are lagging behind. Bytesview is one such CX analytics platform that can help you with these insights so you can boost your revenues.

Driven by AI and native language processing for 30+ different languages, the Bytesview solution is available in aspect models that can be customized for a perfect fit for your specific business concerns, hence giving you high-precision insights, quickly and accurately.

How sentiment Analysis can help Improve Customer Experience?

How do customers feel about the product, services, and experience they receive from your brand?

These insights can help you improve customer perception of your company and company offerings and you can implement what is required and eliminate the unnecessary obstructions. By understanding the pain points, you can use the data for your leverage by making customer-centric decisions to improve customer experience.

By allowing you to analyze more data sources, and gathering fine-grained insights by eliminating human bias from feedback analysis, it can provide you with highly precise CX insights. These data-backed insights are what you need to develop better strategies to increase customer engagement.

Let’s see in what ways Sentiment Analysis helps an organization to improve customer experience.

Identify sources of customer dissatisfaction

Customers often share opinions about a business’s products or services on different social media platforms which they might not share directly with the concerned organization. As a result, businesses might not know of these issues or challenges their customers are complaining about.

Sentiment analysis eliminates these problems by allowing organizations to analyze data from different sources where customers share their opinion. This allows the organizations to get to the root of specific sources of customer dissatisfaction, and they can use these CX insights to eliminate these issues.

2.Social Media Tracking

Customers express their views and concern with products, services, or a brand in general on different social media platforms.

Sentiment analysis is being used to track customers’ feelings about their experiences in real-time. This can help the organizations detect emotions behind the comments, feedbacks which help in understanding the pain points in customer experience. As a result, better solutions can be offered for immediate solutions.

3.Improved Customer Services

Customer Support comes to the rescue when customers seek help when they have issues to resolve. Sentiment Analysis can help your agents understand the customer’s pain point and thus necessary measures can be implemented based on that.

Customer sentiment scores are measured by evaluating nuances in support tickets with the help of sentiment analysis. This helps streamline the process of assigning the right case to the right agent.

4.Personalize Customer Communication

Customers’ emotions can be decoded with sentiment analysis which can help you personalize the interaction in order to improve overall CX. Customers expect personalized messages from organizations and expect relevant experiences tailored to their needs and demands.

The past, as well as live interactions among customers and agents, can be identified with the help of sentiment analysis, which helps to provide agents with insight that they can use to customize the customer experience. What type of messages triggers positive or negative emotions in customers can also be identified by the sentiment analysis.

In both cases, friendly responses can be customized that can positively impact customers’ emotions and leave them with a positive experience.

Conclusion

Sentiment Analysis craves the path to reduce any potential upcoming issue that can upset your customers and reach out to them the earliest and offer them a delightful experience. The text-mining tool identifies the emotional triggers and helps you optimize your customer service to engage loyal customers by proving them personalized and positive customer experience.

Sentiment Analysis challenges and ways to overcome them.

shreya sahani — Fri, 08 Apr 2022 13:13:11 +0000

A huge amount of data is being generated from forums, blogs, social sites, and other various platforms where people share their opinion. Gathering information manually about user-generated data is time-consuming, that’s why companies and organizations are opting for automatic sentiment analysis methods to help them understand it.

What are the challenges in Sentiment Analysis and how to overcome them?

When it comes to challenges regarding sentiment analysis, there are a few things that companies struggle with in order to obtain sentiment analysis accuracy. Sentiment analysis becomes difficult in natural language processing simply because the system has to be trained to understand, analyze and process emotions in the text as a human brain does. As data science is continuing to evolve, sentiment analysis softwares are becoming more and more able to tackle these issues better. Here are the main roadblocks in analyzing sentiment and how technologies/sentiment analysis APIs like Bytesview, monkeylearn, aylien can be used to solve them.

Challenge 1: Sarcasm

People use sarcasm and irony in casual conversations on social media. This act of expressing negative sentiment using backhanded compliments allows sarcasm to easily cheat sentiment analysis models unless they’re specifically designed to take its possibility into account.

solution- A good sentiment analysis API like Bytesview or any other API will be able to detect the context of the language used in creating actual sentiment when something is posted. Bytesview is trained for 30+ language datasets on which the sentiment analysis model works which gives precise and accurate results.

Challenge 2: Idioms

Machine learning programs don’t understand a figure of speech usually. Idioms boggle the algorithm because it understands things in the literal sense. the sentence can be misconstrued by the algorithm or even ignored when an idiom is being used in a comment or review. The sentiment analysis platform needs to be trained in understanding idioms to overcome this problem. The problem becomes manifold when it comes to multiple languages.

solution- The only way this challenge can be solved with sentiment analysis accuracy is if the neural networks of the API are trained enough to understand and interpret idioms. Idioms are mapped according to nouns that denote emotions like joy, anger, success, determination, etc, and then the models are trained accordingly, only then can a tool for analyzing sentiment give accurate insights from such text.

Challenge 3: Polarity

Sometimes, a given sentence or document — or whatever unit of text we would like to analyze —exhibits multipolarity. In these cases, having only the total result of the analysis can be misleading, sometimes phrases get left out, which dilutes the sentiment score.

solution- A good sentiment analysis tool can easily figure out these words and mid-polar phrases in order to give an overall view of the comment. In this context, topic-based sentiment analysis can give a well-rounded analysis, but with aspect-based sentiment analysis, one can get an in-depth view of many aspects of a comment.

Challenge 4: Comparative Sentences

Comparative sentences are tricky because they may not always give an opinion. often it has to be deduced. For example, when somebody writes, “the laptop is lighter than the desktop”, here the sentence does not mention any negative or positive emotion but rather states a relative ordering in terms of the weight of the two entities.

solution- In this case, sentiment analysis accuracy can be achieved when a sentiment model compares the extent to which an entity has one property to a greater or lesser extent than another property, and then tie that to negative or positive sentiment. Training the AI machine to actually pull together information from its knowledge graph and analyze the relationship between entities, words, and emotions is the legitimate solution to this.

Challenge 5: Multilingual Data

Multilingual sentiment analysis constitutes all the problems one can think of in layman's terms and it gets compounded when a cocktail of languages is thrown in. Every language needs a unique part-of-speech tagger, lemmatizer, and grammatical constructs to understand negations.

solution- The sentiment analysis model needs to have a uniquely trained platform and named entity recognition model for each language like BytesView has. There is no shortcut to this because the model needs to be trained in each language manually. It is a time-consuming process that needs diligence and precision, but the results will give you the highest sentiment analysis accuracy scores possible.

Wrapping up

Every challenge we’ve covered can be easily tackled through the use of a strong sentiment analysis API. Bytesview’s software can analyze and report on customer sentiment, from comment tone to phrases with multipolarity to employee feedback and most of the things related. All of this is done through a wide range of AI-based techniques such as text analytics, natural language processing, and named entity recognition etc. Bytesview sentiment analysis platform understands multiple languages natively, which means wherever your business is, and whoever your customers are, you can get deep dives into consumer insights.

Sentiment Analysis Data sources for Strategic Text Analysis

shreya sahani — Tue, 29 Mar 2022 05:51:10 +0000

Sentiment analysis or opinion mining is a natural language processing (NLP) technique used to analyze text data. It analyzes textual data to determine whether it is positive, negative, or neutral. Brands and businesses often use sentiment analysis to analyze public opinion about their brand or product sentiment via customer feedback. It also helps them understand the current market trends and needs of the customers.

What are the sources of gathering sentiment analysis data?

The efficient way to build an effective sentiment analysis solution is, by analyzing various datasets and testing different approaches. You will need to accumulate a substantial volume of data to perform your research and testing, you can either gather the data by yourself from sources like review, social media, employee interaction data, etc or you can take help from sentiment analysis APIs like Bytesview which gathers text data from multiple sources (reviews, opinions, suggestions, social posts, support queries, etc ) and transform it into actionable insights to help make data-driven decisions.

Let’s discuss each source of gathering sentiment analysis data in detail -

Customer Reviews- Sentiment analysis data can be gathered from review websites such as Superpages, Google reviews, Clutch, Demandforce, etc. You can further get narrow down your data search specific to an industry such as Glassdoor, Vault, JobAdvisor, for employee satisfaction and hospitality; Yelp, Yahoo! Local, etc for restaurants and local businesses.

Videos and News Articles- Videos and articles from news websites are apt sources of sentiment analysis data for various business needs, especially brand reputation monitoring. An organization can compile all relevant videos in its repository and analyze them with the help of video AI.

Social Media- Sentiment analysis data sources can be gathered from social media including platforms such as YouTube, TikTok, Twitter, Tumblr, Instagram Live, WeChat, Youku, Reddit, and others. Interestingly, relevant information can be extracted from not only comments but the videos themselves through social media video content analysis.

Surveys- Surveys are one of the best sentiment analysis data sources to get meaningful data. Surveys can be sent by chats, emails, mobile messages, and even phone calls. Open-ended survey questions tend to allow people to express themselves more freely, so that’s where the major chunk of your insights will come from. Bytesview even integrates its sentiment analysis API with survey software to provide sentiment analysis-driven survey intelligence.

Insights from sentiment analysis data sources help a business beyond measures. Sentiment analysis NLP (natural language processing) of this data is vital for formulating necessary corporate and marketing strategies.

If done right, sentiment analysis can be crucial to a company in the following ways-

Capturing new markets
Competitor analysis
Employee engagement
Product differentiation
Business intelligence
Better sales conversions
Capturing new markets
Increase brand value and visibility

Wrapping Up

Information from the right sentiment analysis data sources can give organizations a deeper understanding of their target customers, which can translate into more profitable business strategies. Bytesview’s sentiment analysis platform analyzes such data in more than 30+ languages and dialects and gives you valuable insights.

Topic Labeling: A Beginner's Guide.

shreya sahani — Thu, 24 Mar 2022 06:26:08 +0000

Topic labeling is a machine learning and NLP technique that helps in extracting meaning from a large volume of unstructured texts on the basis of recurrent themes or topics.

Businesses generate a large volume of documents and unstructured text on a daily basis such as social media posts, emails, forum discussions, reviews, and customer support tickets. But when it comes to analyzing and making sense of this data, it is far too big to process manually. If you go ahead and start analyzing the data manually, it will be too time-consuming and repetitive that you are bound to make mistakes, plus it won’t scale much.

This is where the relevance and importance of topic labeling come into play. AI-guided topic analysis can make it much easier and faster to accurately analyze and extract large volumes of data. All you need to do is to sign up for a good topic analysis providing software such as Bytesview, monkeylearn, lexalytics, etc to get through. Now let’s dive deep into the topic.

What is Topic Labeling?

Topic labeling, also called topic modeling, topic detection, or topic extraction, is a machine learning and NLP technique that examines and understands large collections of text data by assigning “tags” or categorizing documents/paragraphs based on the topic or theme of the text.

Topic Labeling tools-

1. BytesView- BytesView is an efficient tool that you can use to automate the classification of documents with topic labeling and text categorization. It helps you segregate documents by identifying clusters of words from unstructured text data within minutes with guaranteed accuracy.

2.Monkeylearn- MonkeyLearn provides a simple graphical interface where users can create customized text classification and extraction analysis by training machine learning models such as topic detection, keyword extraction, and more. It can be integrated with hundreds of other applications through its direct integrations and open API.

Topic labeling approaches

NLP topic modeling and NLP topic classification are the two most common approaches for topic analysis with machine learning.

topic-modeling- It is an unsupervised machine learning technique. It can infer clusters and patterns comparable utterances without the need for subject tags or training data in advance. However, there is a drawback to this type of algorithm: it lacks accuracy.

topic classification- Topic classification requires knowing the topics before starting analyzing. You need to tag a substantial volume of data to train the classifier. Even though the approach is more time-consuming than topic modeling, in the long run, it is way more accurate. It all depends on the quality of the data you train it with.

scope and levels at which you can apply topic analysis

sentence-level: the topic of a single sentence is derived using this model. For example, the topic of a fashion magazine headline.

sub-sentence level: the topic of sub-expressions from within a sentence can be derived from this topic model. For example, different topics within a single sentence from feedback.

document-level: different topics from within a complete text is fetched from this topic model. For example, the topics of a news article or a blog.

Why is topic labeling important?

Organizations generate and accumulate massive amounts of unstructured text data every day. Automated topic labeling approaches provide immense benefits including making better decisions, finding new patterns, streamlining operations, and identifying trends.
When it comes to sorting through all this data, machine learning models are crucial. We can efficiently scan large amounts of text using topic identification and can find out what our clients are talking about.

Applications of Topic Labeling

Product Analytics- Making improvements from scratch to an existing product or developing is not easy. one needs to get insights about the consumers’ needs, finding out which features are most sought-after, and which combination of features works the most. You need to dive into the data of your competitors to get these insights. You can analyze their services and products, how customers respond to them, which improvements do they seek, which features are unnecessary etc.

Brand Monitoring- topic identification and analysis help you to get insights about your brand by detecting and tracking the different areas of your business people are discussing the most. The real-time topic analysis allows you to keep track of your brand image and also helps to monitor your competitors and detect the latest trends in your industry.

Social Media Monitoring- Social media is a vast pool of user-generated information often related to brands, products, organizations, and services, with these immense volumes of textual data, even finding the information related to a relevant topic is difficult, let alone analyze it. you can use topic analysis to sort this problem out and gain insights you actually need to focus on.

Wrapping Up

The topic analysis has made it possible to detect subjects and topics within huge sets of text data simple and efficient way. It allows you to automate your business. Start training your custom topic labeling model today.

Topic Labeling: A Definitive Guide

shreya sahani — Fri, 11 Mar 2022 10:09:36 +0000

Topic Labeling is an (NLP) Natural Language Processing technique that enables you to automate tagging and organizing massive values of textual data based on the topic or theme.

On a daily basis, businesses generate a large volume of documents and unstructured text such as emails, social media posts, reviews, forum discussions, and customer support tickets. But when it comes to analyzing and making sense of this data, it is far too big to process manually. Even if you go ahead and start analyzing the data manually, it will be too time-consuming and you are bound to make mistakes.

This is where topic labeling can help you out. It can make it much easier and faster to accurately analyze and extract large volumes of data. In this blog, we will discuss what topic labeling is and how you can use it to analyze unstructured text.

What is Topic Labeling?

Topic Labeling is also known as topic extraction is a machine learning and NLP technique that examines and organizes large volumes of unstructured text data. It can tag and categorize documents or even each paragraph based on the topic or theme of the text.

Read the full article here:https://www.bytesview.com/blog/topic-labeling/

Social Listening Tools: Everything you need to know and the best tools to start.

shreya sahani — Thu, 24 Feb 2022 07:21:00 +0000

Social listening is tracking and analyzing all social media platforms for conversations related to your brand and using the insights which you found out to build and discover opportunities to act.

It is a great way to keep tabs on what customers, prospects, feedbacks, etc agave to say about your business/brand/product/services, etc.

It entails monitoring your brand's social media profiles for customer feedback, direct or indirect mentions of your brand, any conversations with relevant topics, keywords, industries, or, competitors. This is followed by an analysis of that information. The resulting conclusions will help you determine the best ways to improve your brand awareness, social media strategy, and social presence.

Now you may be wondering, which are the best social listening tools. The market of social media monitoring & listening software is extremely diverse and it might be difficult to decide what social listening platform will fit your business best and figure out how to optimize it for your social media marketing strategy. Here are the few ones with different functionalities and expertise.

1.Bytesview

BytesView is an advanced machine learning and NLP-based text analysis tool. It can compile and analyze large volumes of text data from multiple information sources with ease. The various text mining and analysis models can help you analyze and extract valuable insights from unstructured text. They also offer API services that can help you train custom data analysis models with data specific to your organization to increase accuracy and efficiency. The dedicated plugins make it much easier to integrate unstructured text with our system for analysis.

2.Brandwatch

Brandwatch is a consumer intelligence and social media listening platform. Brandwatch is an enterprise social intelligence leader, empowering over 2,000 of the planet’s most admired brands and agencies to make insightful, data-driven business decisions.

3.Awario

Awario is a social listening tool that gives brands access to data that matters to their business: insights on their customers, market, and competitors. Our mission is to make social listening, social media analytics, and competitive intelligence affordable for businesses of any size - from startups and small businesses to marketing agencies and international corporations.

4.Mention

Mention is the leading tool for brand monitoring, social listening, and reputation management for enterprises and agencies

The application creates alerts for clients’ brand, industry, company, name, or competitors as well as inform in real-time about any mentions on the web and social web. Its application features include media and social monitoring, anti-noise technology, and statistics and data expert tools.

Wrapping up

I would always recommend reviewing product and pricing pages in-depth for any tool you're looking to incorporate on your team. Additionally, don't be afraid to experiment with more than one social media tracking tool to determine which will effectively meet your needs and help you achieve your goals.

References
https://medium.com/@shreya_88085/social-listening-tools-everything-you-need-to-know-and-the-best-tools-to-start-367bdbc3904c
https://www.bytesview.com/
https://awario.com/

The Ultimate Guide to best Text Analytics solutions.

shreya sahani — Tue, 25 Jan 2022 11:07:17 +0000

As a budding marketing professional, I always wondered if all the text analysis solutions are created equal? I got an empathetic ‘’NO’’as the answer.

I got to know that each text analytics platform has a specific offering and an ideal client for their technology. The text analytics market is well established, but with so many options to choose from, how will you get to know which platform is most suitable for your organization? This short article aims to help you decide.

In no particular order -other than to put my favorite first- here are the best text analytics tool in the market….

Bytesview

Bytesview is a ready-to-use comprehensive text analytics solution. However, you can also choose from the various custom integration options if needed. It also enables easy integration of data with various in-built integrations such as Zendesk, Zapier, Excel, and Google sheets.If you have the resources and time to code, you can use the BytesView API to build your own custom sentiment analysis model.

Rapidminer

RapidMiner markets itself as a machine learning company for enterprise-level clients looking to “drive revenue, reduce costs, and avoid risks.” With a focus on data science teams, their platforms offer customization and specialization equivalent to the IBM Watson suite, but with a scope well beyond text analytics.

RapidMiner’s platform consists of two main elements: the core engine RapidMiner Server and the visualization suite, RapidMiner Studio.

MeaningCloud

MeaningCloud Sentiment Analysis API program does aspect-based sentiment analysis on the user’s input to find out if a specific topic draws positive, negative, or neutral sentiments.

Some of MeaningCloud’s best features include global sentiment detection understanding which elements are opinion and which are facts and determining how the customer felt about every individual sentence in the text

MonkeyLearn

Monkeylearn is another excellent text analysis tool that offers a ready-to-use sentiment analysis solution. It offers plugins for Zendesk, Google Sheets, and more for easy integration of data.

Additionally, if you know how to code, you can use the MonkeyLearn API to build your custom sentiment analysis model. You can use data related to your industry or business organization to train the model and increase accuracy.

Takeaway

Text analytics extracts cutting-edge data that can be translated into insights on consumer intent, social media sentiment, employee satisfaction, competitor intelligence… the list goes on, access your requirements before you choose one to get started.

Voice of Customers Analysis: Why do you need it and How to set it up?

shreya sahani — Tue, 18 Jan 2022 09:16:09 +0000

Voice of customers, why do you need it?

Customers expect more than ever from the brands they use. They expect products and services to perform exactly to their needs–easy to set up, easy to use, etc–and more personalized and empathetic customer service.

In this guide, learn how to create your voice of customer program, so you can get to know your customers better.read more..
https://www.bytesview.com/blog/voice-of-customers-analytics/

Analyze big chunks of data with these best paid and free sentiment analysis tools to get valuable insights.

shreya sahani — Fri, 07 Jan 2022 05:53:53 +0000

Analyze text data gathered from multiple sources and transform it into actionable insights to help make data-driven decisions.
visit:https://www.bytesview.com/blog/best-sentiment-analysis-tools-2021/

The Top 10 Text Analysis APIs to Use in 2021

shreya sahani — Thu, 07 Oct 2021 09:27:13 +0000

Text analysis, also known as text mining is a process that enables you to extract valuable insights from unstructured text. It uses natural language processing (NLP) to break down the data and machine learning to extract machine-readable insights from textual data.

Even if you have various options, choosing the right API can be difficult. To help you make it easier, this article lists some of the most effective text analysis APIs that you can use.

Link - https://www.bytesview.com/blog/top-10-text-analysis-apis-2021/

Sentiment Analysis: Everything You Need to Know

shreya sahani — Tue, 14 Sep 2021 07:57:01 +0000

Learn what sentiment analysis is, how it works, the challenges it faces, and how to use it to improve your products, meet customer expectations and improve decision making in this post.

Read the full blog here - https://www.bytesview.com/blog/sentiment-analysis/