Sentiment Analysis In Action !EXCLUSIVE!
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Almost every industry, company or organization today is going through some form of digital transformation that results in greater and greater quantities of both structured and unstructured data. The biggest challenge that companies have is turning their unusable, unstructured data into useful insights that can help them make data driven decisions, create operational efficiencies, value improvement, and overall competitive advantage. Any industry can benefit from text analytics and sentiment analysis, as all industries collect data and require that it is transformed into actionable, tangible intelligence that can be applied to drive change. Here are some real-world use cases of sentiment analysis across industries and geographies that demonstrate this.
A European mobile network operator wanted to integrate its call center software so it could track and analyse all customer service representative (CSR) interactions. They wanted to get a sense as to what the tipping point of a certain number of negative interactions was, that was causing customer attrition.
This was not all. On subsequent calls with the CSRs, a historical data would get pulled up from the database, and so the CSR was able to offer the customer promotions and enquire if they were happy with the service. Applying sentiment analysis in business for voice of customer analysis not only increased customer satisfaction but also created more goodwill, leading to more customers for the bank.
A great example of semantic analysis in business is when Repustate was approached by a government ministry in Asia-Pacific. The government wanted to increase the efficiency of its public services and be proactive in order to serve its citizens better. It wanted to know the main issues that citizens faced, ranging from traffic jams, to erratic service at the passport office. The government wanted to ensure that not only were the problems solved, but that citizens did not face similar problems in the future.
A large Hedge Fund Company specializing in the Asia-Pacific market wanted to analyse market data in real time. The main problem they faced was that most of the information that came across through newswires and other sources was in Mandarin. High volatility in the financial market means the need for lightning quick reflexes to make transactions in sub-second frequencies is intense. But not being able to make sense of the data, doubled down by the language barrier, was causing the Hedge Fund company serious problems.Repustate was able to provide the company with a solution to this issue.
The insights gained from sentiment analysis can help a company bring accurate change and transformation of their business. It can be in areas that are either creating the most negative sentiment such as product features, price, return policies, customer service, or prices, or areas that stakeholders are most positive about such as net banking, price match, and such. Overall, these strategic measures help businesses:
Sentiment analysis examples in various industries with real-world requirements, prove that the advantages of sentiment monitoring are pivotal to a modern-day organization. So to sum up, along with the areas mentioned earlier on, the three broad areas in which sentiment monitoring can bring huge return on investments for businesses are:
Able to predict the future based on high-precision artificial intelligence is the next frontier in business. From predicting a fall in oil prices due to an impending political instability in a region, to knowing which programmes will be popular in which markets for an over-the-top content platform like Netflix or even the BBC - the areas in market trend that sentiment analysis can cover are innumerable. Checkout this example of how TikTok trend analysis is done in clothing retail sector using video content analysis tool.
Having an edge over your competitor means having all the information about how they affect you, at your fingertips, at any given time. Sentiment analysis in business allows you to find gaps in your marketing strategy, manage your brand reputation, and zero in on key areas where customer sentiments are positive or negative. Companies can work on audience engagement, contextualize and granulate key performance indicators, and build better messaging for their marketing and advertising campaigns.
At the end of the day, businesses can grow only when they truly understand the people using their products or services. This in itself is a momentous task as the human experience comes with a wide range of complicated emotions and interactions. Artificial Intelligence gives us the capability to delve deep into not only segregating these emotions, but also creating a threshold on which to use this emotional intelligence as a benchmark. Repustate is able to bring this intelligence to light via a simple, easy-to-use, sentiment analysis dashboard, where businesses can not only view the data, but track and simplify complex data sets too. The monitoring tool can also help businesses gain a quick insight into current and future trends by converting the data into charts, graphs, and tables.
Repustate has been refining this craft of simplifying big data for clients for more than a decade. Through complex AI-powered, machine-learning based solutions for sentiment analysis in more than 23 languages that it analyzes natively, Repustate gives more accurate results, and with better understanding of the underlying problems.
We are very satisfied with the accuracy of Repustate's Arabic sentiment analysis, as well as their and support which helped us to successfully deliver the requirements of our clients in the government and private sector.
Data cleaning techniques are essential to getting accurate results when you analyze data for various purposes, such as customer experience insights, brand monitoring, market research, or measuring employee satisfaction.
Sentiment refers to the positivity or negativity expressed in text. Sentiment analysis provides an effective way to evaluate written or spoken language to determine if the expression is favorable, unfavorable, or neutral, and to what degree. Because of this, it gives a useful indication of how the customer felt about their experience.
Sentiment analysis is part of the greater umbrella of text mining, also known as text analysis. This type of analysis extracts meaning from many sources of text, such as surveys, reviews, public social media, and even articles on the Web. A score is then assigned to each clause based on the sentiment expressed in the text. For example, -1 for negative sentiment and +1 for positive sentiment. This is done using natural language processing (NLP).
We live in a world where huge amounts of written information are produced and published every moment, thanks to the internet, news articles, social media, and digital communications. Sentiment analysis can help companies keep track of how their brands and products are perceived, both at key moments and over a period of time.
Not all sentiment analysis is done the same way. There are different ways to approach it and a range of different algorithms and processes that can be used to do the job depending on the context of use and the desired outcome.
Sentiment analysis uses machine learning, statistics, and natural language processing (NLP) to find out how people think and feel on a macro scale. Sentiment analysis tools take written content and process it to unearth the positivity or negativity of the expression.
Another challenge is to decide how language is interpreted since this is very subjective and varies between individuals. What sounds positive to one person might sound negative or even neutral to someone else. In designing algorithms for sentiment analysis, data scientists must think creatively in order to build useful and reliable tools.
Sentiment classification requires your sentiment analysis tools to be sophisticated enough to understand not only when a data snippet is positive or negative, but how to extrapolate sentiment even when both positive and negative words are used. On top of that, it needs to be able to understand context and complications such as sarcasm or irony.
Human beings are complicated, and how we express ourselves can be similarly complex. Many types of sentiment analysis tools use a simple view of polarity (positive/neutral/negative), which means much of the meaning behind the data is lost.
This is where training natural language processing (NLP) algorithms come in. Natural language processing is a way of mimicking the human understanding of language, meaning context becomes more readily understood by your sentiment analysis tool.
Sentiment analysis algorithms are trained using this system over time, using deep learning to understand instances with context and apply that learning to future data. This is why a sophisticated sentiment analysis tool can help you to not only analyze vast volumes of data more quickly but also discern what context is common or important to your customers.
Monitoring tools ingest publicly available social media data on platforms such as Twitter and Facebook for brand mentions and assign sentiment scores accordingly. This has its upsides as well considering users are highly likely to take their uninhibited feedback to social media. 2b1af7f3a8