Lily Thomas

Lily Thomas

1653560868

14 Key Insights An Advanced Sentiment Analysis Software Shares

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A sentiment analysis software uses AI and machine learning to analyze text, audio, and video conversations to identify the tone, intent, and emotion behind every message. While a regular sentiment analysis platform will give the aggregate score of the emotion around a product or a brand, an advanced sentiment analysis solution offers much deeper insights that have a genuine effect on your action plan for growth. In this article, we check out the 14 key insights that such a sentiment analysis software shares.

What Is Sentiment Analysis?

Sentiment analysis is the method of understanding feelings expressed in data through machine learning tasks such as text analysis, video content analysis, natural language processing, semantic clustering, and others. A sentiment analysis software gives organizations data-driven insights to understand current market trends, predict new consumer trends, gaps in customer engagement, product and service improvement ideas, as well as insights to enhance employee experience.

What Are The Advanced Features Of A Sentiment Analysis Platform?

A sentiment analysis dashboard can give a business very granular insights from the data it processes. To do so, it uses several of its advanced features, such as those below:

1. Ability to choose industry-based aspects (& create customer aspect)

The accuracy of the insights you get from any machine learning platform comes from its named entity recognition (NER) capability. NER makes sure that every entity (person, organization, logo, place, currency, etc.) that is relevant to your industry is extracted for semantic and sentiment analysis.

Each industry has its own aspects, such as a restaurant will have its own aspects (food, drinks, ambiance, price, customer service, etc), while a bank will have aspects pertaining to its industry (salary, money, deposit, withdrawal, etc.). An advanced sentiment analysis platform will not only allow you to choose industry aspects based on your industry but also custom-create aspects that are niche and unique to you.

2. Ability to evaluate multiple social media sources

An advanced sentiment analysis software will allow you to collect and analyze data from a variety of sources, both text-based as well as video-centric. This includes social media platforms as well as other online platforms for product reviews. For example, the Repustate sentiment analysis platform, Repustate IQ, is capable of analyzing data from such sources as TikTok, Douyin, Amazon Reviews, Instagram, Facebook, Twitter, and others.

3. Ability to analyze video content

In order to gain a thorough understanding of your customer base and the market perception of your product and brand, you need to gather as much information as possible. The more varied the data sources the better.

In this regard, it is very important that the sentiment analysis software is able to extract findings from video content as well. Video AI allows you to get insights from YouTube video analysis or obtain TikTok insights as easily as you would get from any text-based source such as Twitter, TrustPilot, or a news website.

What Insights Does A Sentiment Analysis Software Share?

A sentiment analysis platform gives you insights based on sentiment by language, data source, and ofcourse, the general sentiment overview. You can analyze entities, see their frequency, get granular aspect-based sentiment, and see the change in sentiment over time. There are several other key insights you can get. We discuss them all in detail below:

1. General sentiment overview & sentiment score

One of the most basic, yet principle things you can see in the insights generated by a sentiment analysis API is the general sentiment overview of the subject you are analyzing. The sentiment analysis software will crawl the internet for all relevant data or will analyze a data sheet that you have manually uploaded, and give you the number of total documents it has analyzed and the sentiments in color codes. In this case, green for positive, red for negative, and blue for neutral.

2. Aspect-based sentiment breakdown

A very important result that the sentiment analysis platform gives is a fine-grained analysis of every single aspect it discovers and categorizes from the data. The only true way a business can successfully remain relevant and thrive in a tough competitive environment is when it reads the room, in this case, the voice of its customers.

Had shoe company Aldo listened to what its customers were saying about the tawdry quality of their shoes and bags, as well the customer service, they would not be facing the situation they are in today. In contrast, 80-year-old business Browns Shoes remains strong as ever as it continues to evolve by listening to its customers and understanding market trends.

Aspect-based sentiment analysis gives you this power to carve your own niche by keeping an eye on every single detail about your brand.

3. Aspect occurrence frequency rates

Another interesting thing the sentiment analysis platform shows you is how frequently an aspect appears in the data. This gives you an idea of which aspect of your product, service, or overall brand, is being talked about the most. See image below.

4. Named entities, classifications, and entity frequency

The sentiment analysis software applies named entity recognition and gives you entities that it has discovered, extracted, and analyzed. For example, it will tell you that “love” is an emotion a person has, or that French is the nationality of a person (see image below).

It is interesting to note, that as it classifies these entities, it realizes the difference between “French” the language, and “French” the people. This is due to its capability to derive intelligence from semantics and contextual reference.

This is a vital advantage that Repustate IQ has over other similar emotion mining and analysis platforms.

5. Sentiment by keyword

You can also get sentiment by keyword. The sentiment analysis platform processes all the data related to customer experience, healthcare, the voice of the employee, brand monitoring, and such, and immediately picks out the sentiment attached to important words, as in, the most commonly occurring words and phrases, in that project.

This is useful because you get a very holistic picture of the data you are analyzing and even pick up things that didn’t even occur to you as a brand.

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Sofia  Maggio

Sofia Maggio

1626077565

Sentiment Analysis in Python using Machine Learning

Sentiment analysis or opinion mining is a simple task of understanding the emotions of the writer of a particular text. What was the intent of the writer when writing a certain thing?

We use various natural language processing (NLP) and text analysis tools to figure out what could be subjective information. We need to identify, extract and quantify such details from the text for easier classification and working with the data.

But why do we need sentiment analysis?

Sentiment analysis serves as a fundamental aspect of dealing with customers on online portals and websites for the companies. They do this all the time to classify a comment as a query, complaint, suggestion, opinion, or just love for a product. This way they can easily sort through the comments or questions and prioritize what they need to handle first and even order them in a way that looks better. Companies sometimes even try to delete content that has a negative sentiment attached to it.

It is an easy way to understand and analyze public reception and perception of different ideas and concepts, or a newly launched product, maybe an event or a government policy.

Emotion understanding and sentiment analysis play a huge role in collaborative filtering based recommendation systems. Grouping together people who have similar reactions to a certain product and showing them related products. Like recommending movies to people by grouping them with others that have similar perceptions for a certain show or movie.

Lastly, they are also used for spam filtering and removing unwanted content.

How does sentiment analysis work?

NLP or natural language processing is the basic concept on which sentiment analysis is built upon. Natural language processing is a superclass of sentiment analysis that deals with understanding all kinds of things from a piece of text.

NLP is the branch of AI dealing with texts, giving machines the ability to understand and derive from the text. For tasks such as virtual assistant, query solving, creating and maintaining human-like conversations, summarizing texts, spam detection, sentiment analysis, etc. it includes everything from counting the number of words to a machine writing a story, indistinguishable from human texts.

Sentiment analysis can be classified into various categories based on various criteria. Depending upon the scope it can be classified into document-level sentiment analysis, sentence level sentiment analysis, and sub sentence level or phrase level sentiment analysis.

Also, a very common classification is based on what needs to be done with the data or the reason for sentiment analysis. Examples of which are

  • Simple classification of text into positive, negative or neutral. It may also advance into fine grained answers like very positive or moderately positive.
  • Aspect-based sentiment analysis- where we figure out the sentiment along with a specific aspect it is related to. Like identifying sentiments regarding various aspects or parts of a car in user reviews, identifying what feature or aspect was appreciated or disliked.
  • The sentiment along with an action associated with it. Like mails written to customer support. Understanding if it is a query or complaint or suggestion etc

Based on what needs to be done and what kind of data we need to work with there are two major methods of tackling this problem.

  • Matching rules based sentiment analysis: There is a predefined list of words for each type of sentiment needed and then the text or document is matched with the lists. The algorithm then determines which type of words or which sentiment is more prevalent in it.
  • This type of rule based sentiment analysis is easy to implement, but lacks flexibility and does not account for context.
  • Automatic sentiment analysis: They are mostly based on supervised machine learning algorithms and are actually very useful in understanding complicated texts. Algorithms in this category include support vector machine, linear regression, rnn, and its types. This is what we are gonna explore and learn more about.

In this machine learning project, we will use recurrent neural network for sentiment analysis in python.

#machine learning tutorials #machine learning project #machine learning sentiment analysis #python sentiment analysis #sentiment analysis

Custom Software vs Off-the-shelf Software: How to select a better one for your business?

Custom Software or Off-the-shelf software, the question in mind for many business personnel. Read this blog to get help to make the right decision that will benefit your business.
For a business that wants to upgrade and modernize itself with the help of software, a common dilemma it is whether to go for custom-made software or opt for off-the-shelf software. You can find many top software development companies worldwide, but before that all, you should first decide the type of software –an off-the-shelf software or a custom one.
This blog aims to overcome the dilemma and accord some clarity to a business looking to automate its business processes.

#custom software vs off-the-shelf software #custom software development companies #top software development companies #off-the-shelf software development #customized software solution #custom software development

Christa  Stehr

Christa Stehr

1594456938

Offshore Software Development - Best Practices

With the rise of globalization and the worldwide lockdown due to the pandemic, most of the work has been done by remote working processes and professionals from their homes. This lockdown has proved the efficiency of remote development and enhanced the trust in offshore software development industry.

To make the most out of the benefits of offshore software development, you should understand the crucial factors that affect offshore development. This is why you should read this guide for the best practices when hiring an offshore software development company. Despite the size and the industry of the business, offshore software development is not beneficial for every entrepreneur in many aspects to make the optimum use of talents in technology across the globe.

Here are some of the top reasons why offshore development is beneficial for your business.

  • Offshore development teams can work on flexible timing to provide you with the best possible software development practices.
  • Get access to the talents across the world from your home to develop the top of the line software with the help of offshore development companies.
  • Assured high quality and next-generation technology expertise with duly NDA signed with respect to the priorities of the business.
  • With flexible recruitment models, you can hire the freelance developers, remote development team, or an entire offshore development company with respect to the size of your business.
  • Build high-end software applications from one corner of the world by hiring software developers across the world.
  • Get immediate access to the best resources without hiring them on a permanent basis.

To avail of all these benefits, you should have clear goals, a list of requirements, and features that are mandatory for your software product.

Here are a few tips to help you find the best offshore software development company. Build a top-notch software application by following the listed best practices.

#web development #how to start offshore software development company #offshore meaning #offshore software development best practices #offshore software development company #offshore software development company in india #offshore software development cost #offshore software development statistics #outsource software development

Origin Scale

Origin Scale

1615535784

Purchasing & Replacement Software Solutions | Originscale

With Originscale, you’ve got reliable software providing dynamic inventory insights at your fingertips including raw materials and finished products that help you prevent missed production timelines, sales, avoid stockouts, and free up cash by optimizing operations so you can reinvest in your business.
Read more: https://www.originscale.io/purchasingreplenishment

#purchasing software solutions #purchase order software #purchasing software #free purchasing software #purchase order management software #purchasing software for manufacturing

Software Developer vs Software Engineer — Differences: Bogus or Real?

Software Developers vs Software Engineers

Personally, it pisses me off. Every time I see an article on this topic, my emotional bank account gets robbed. They are all about SEO. Inappropriate keywords squeezed into tiny sentences just to get better rankings. No intent to entertain or enlighten the reader whatsoever. Sometimes, such articles can even be outright wrong.

And even though the purpose of this blog post can be to generate traffic, I tried to make it more of a meaningful rant than a lifeless academic essay.

So, let’s see how you feel by the time you are done reading this paper.

Without further ado:

Since there are no proper interpretations of both terms, a lot of people use them interchangeably.

However, some companies consider these terms as job titles.

The general “programmer-developer-engineer” trend goes along the lines of:

  • programmer is someone who knows how to code, understands algorithms and can follow instructions. Yet, it doesn’t go further in regards to responsibilities.
  • developer is someone superior to the programmer. Except for coding, they also do design, architecture, and technical documentation of the software component they are building. They might be referred to as leaders, but not necessarily.
  • Finally, an engineer implies that you are the real deal. You’ve graduated with a degree, have some tech knowledge, and preferably experience… and you are capable of designing a software system (a combination of software components your peons, the programmers, have built). You’re like an overseer. You can see the bigger picture. And it’s your responsibility to clearly explain that “picture” to your team.

#devops #software development #programming #software engineering #software developer #programmer #software engineer #software engineering career