• Part-of-speech (POS) tagger: Built to identify conjunctions, subordinate clauses, prepositions and nouns for each language.
• Lemmatization: To recognize and apply rules of conjugating nouns and verbs based on gender.
• Grammatical constructs: Built to define negations and amplifiers to identify negative and positive words.
• Polarity: To determine the negative and positive polarity of words—between -1 and +1—which are aggregated to give the overall sentiment in the data.

A native language model is important because every language has its own etymology, which affects grammar rules. For example, there are no full stops in Thai, Arabic is written right to left and German has gender-neutral pronouns. If an English machine learning model is used to analyze multilingual data, it will use rules applicable to that language and provide incorrect insights. This can lead to failed or ineffective social and digital marketing campaigns that tax resources and reduce return.

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Aspect-based sentiment analysis is a machine learning (ML) technique. It provides granular, actionable insights from customer feedback data by breaking it down into smaller categories to find and extract hidden brand sentiments.

The technique analyzes data from various sources—social media comments and videos, reviews, online publications, and surveys—and can help identify which features and aspects of a business need improvement to increase revenue.

Document-based sentiment analysis analyzes a whole piece of text and provides a single categorization of the sentiment expressed. at the emotions expressed. Topic-based sentiment analysis breaks pieces of text into words and phrases, clusters them in specific topics such as ‘food’ or ‘customer service’ and calculates sentiments for each of them.

Aspect-based sentiment analysis is the most advanced of the three. It mines aspects from data to measure their sentiment and attributes them to the topics that have been previously identified. For example, it will identify aspects such as ‘quick service’, ‘polite staff’ and ‘cleanliness’, measure their sentiment and collate them under the topic “customer service”. Thus giving you topic-based sentiments plus aspect-based ones.

A machine learning model built on industry aspects provides higher accuracy insights because they are drawn from specifics in the data. This is important because aspects in every industry differ. For example, aspects such as ‘teller’ or ‘savings account’ in the banking industry have no relation to aspects such as ‘food’ or ‘drinks’ in restaurants. With this in-built capability, brands can automatically receive customer sentiment insights about various aspects of their business without having to manually build tags or labels for topics and keywords relevant to their industry.

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With NER, a machine learning model can identify differently written or misspelt words so they are not excluded during tagging. For example, NER helps a social listening software identify that Faceb00k and FB both refer to Facebook and are tagged as a social network.

NER algorithms use statistical models to understand words semantically and contextually. Knowledge graphs further establish the relationship between entities and provide a holistic understanding of the data. This capability makes NER critical in sentiment analysis.

When sentiment analysis algorithms calculate sentiment in voice of customer (VoC) data, they are able to assign a sentiment value to each entity identified by NER. These actionable insights help brands make targeted improvements to their strategies such as developing engaging content, streamlining customer care responses, creating better-targeted ads and more.

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Semantic search algorithms are used by other AI branches and techniques like natural language processing (NLP), natural language understanding (NLU), named entity recognition (NER), knowledge graphs and semantic clustering to perform search tasks. NLP and machine learning (ML) help in keyword extraction and categorize them into semantic clusters. This semantic classification facilitates semantic search algorithms to understand search intent and go beyond exact lexical matches.

Unlike traditional searches that depend on string fields or keyword matches, semantic search employs several tasks such as part-of-speech (POS) tagging, error correction, synonyms, topic and aspect-mapping and others to analyze text. This allows it to present highly precise results based on the most relevant details from multiple sources.

When applied in sentiment analysis, it excludes irrelevant data while identifying and gathering datapoints that are not an exact lexical match but match intent.

This is a key requirement in sentiment analysis to analyze free-form, unstructured content such as social media comments, posts, reviews and open-ended answers in surveys. The more robust semantic clustering is, the more accurate the results are for data sentiment.

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Earlier versions of NLP used rule-based computational linguistics with statistical methods and machine learning to understand and gather insights from social messages, reviews and other data. More recent approaches leverage neural networks and large-language models (LLMs) to accomplish the tasks below

To facilitate NLP, a number of sub-tasks are often conducted, including:

• Tokenization: Text is broken down into smaller single clauses.
• Stemming: Words are broken down into root forms. For example, reading, reader, reads are stemmed into the word “read”.
• Lemmatization: Contextually similar words or degrees are reduced to their root word. For example, better, best and very good are reduced to “good”.
• Stop word removal: Words such as prepositions and articles are removed.
• Part-of-speech-tagging: Nouns, verbs, adjectives, adverbs, pronouns, etc. are tagged.

To facilitate conversational communication with a human, NLP employs two other sub-branches called natural language understanding (NLU) and natural language generation (NLG). NLU comprises algorithms that analyze text to understand words contextually, while NLG helps in generating meaningful words as a human would. Together, they power intelligent chatbots such as ChatGPT.

Here are the main NLP techniques used in business and B2C environments.

1. Text summarizations: NLP algorithms scan large amounts of data and condense the information to provide a summary with key points.
2. Speech recognition: This technique analyzes audio data to translates it into text or maps it to known words. It’s used to caption audio and has been pivotal in empowering the hearing impaired.
3. Machine translations: Automatically translates words in different languages so that users can benefit from non-native information with minimal effort. Google Translate is a good example
4. Question answering systems: NLP algorithms scan data and search for relevant information to provide answers to a user. These systems can be rules-based or based on generative pre-trained models, like ChatGPT, that derive information by accessing publicly available data on the internet.
5. Named entity recognition: Named entity recognition (NER) is an NLP technique that identifies and extracts entities such as people, locations, brands, objects, currencies and such.
6. Semantic search: A search technique that allows a user retrieve information by understanding the intent of the search rather than just using keywords.
7. Sentiment analysis: NLP algorithms that can categorize the emotions in a text to show whether it is positive, negative or neutral and to what extent.
8. Aspect-based sentiment: This advanced technique analyzes sentiment in aspects that have been extracted from topics in a text. This fine-grained view of market sentiment tells brands exactly where they need to improve and what’s going well.

All the above NLP techniques and subtasks work together to provide the right data analytics about customer and brand sentiment from social data or otherwise.

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]]> https://sproutsocial.com/glossary/machine-learning/ Thu, 20 Apr 2023 18:58:26 +0000 https://sproutsocial.com/?post_type=glossary_terms&p=172188/ Machine learning (ML) is a branch of artificial intelligence (AI) and an essential part of data science. It employs statistical methods to classify or Read more...

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]]> Machine learning (ML) is a branch of artificial intelligence (AI) and an essential part of data science. It employs statistical methods to classify or predict patterns in data which can help gather insights for business intelligence, customer experience, market research and other drivers of decision-making.

ML can be supervised, unsupervised and reinforced.

• Supervised learning: Algorithms are trained with industry-specific data to gain insights. This method is most commonly used for business applications.
• Unsupervised learning: Algorithms analyze millions of data points and begin recognizing patterns on their own. It is commonly used in areas such as weather data clustering.
• Reinforcement learning: Advanced ML where algorithms learn to perceive and interpret their environment, and take corrective actions through trial and error. Think: AI-powered robotics.

Machine learning is used in data mining projects for topic, feature and aspect classification, text parsing, semantic clustering and other tasks. These are essential in AI techniques such as named entity recognition (NER), natural language processing (NLP), sentiment analysis, semantic search and others. All of them are critical to extracting insights from big data.

Machine learning models are self-learning because of artificial neural networks (ANNs) that are encoded in them. ANNs are algorithms that understand data points and correlate patterns as humans do, making ML models more intelligent as they process more data.

The more neural layers ANNs have, the greater their capacity to semantically understand data across millions of entities represented in the form of Knowledge Graphs. This advanced form of ANN algorithms translates to Deep Learning (DL)—a subfield that can recognize highly complex patterns in any kind of data for analytical and predictive modeling.

ML models need to be trained to provide insights from big data. When trained with quality data, they can be used successfully for social media sentiment analysis and comment analysis to extract brand, customer and market insights.

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]]> https://sproutsocial.com/glossary/artificial-intelligence/ Thu, 13 Apr 2023 15:42:08 +0000 https://sproutsocial.com/?post_type=glossary_terms&p=171907/ Artificial intelligence (AI) is a field of computer science that mimics the problem-solving and decision making capabilities of the human mind. AI has helped Read more...

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]]> Artificial intelligence (AI) is a field of computer science that mimics the problem-solving and decision making capabilities of the human mind. AI has helped make significant advancements in areas such as disability inclusion, early cancer detection, weather forecasting, security and surveillance and others.

The role of AI in marketing and AI-based capabilities are ubiquitous in our daily lives. Think: Personalized movie recommendations on your favorite streaming service or product suggestions for your next delivery order.

Below are five main subfields that are critical to how AI works

Machine learning: Machine learning (ML) is a branch of AI that uses algorithms that can learn from data to classify or predict patterns in data. ML insights are used to make informed decisions in areas that impact growth such as marketing and business operations. ML can be supervised or unsupervised. Supervised learning needs training data mapped to a known outcome and is the most commonly used in market research, predictive modeling and text parsing.

Neural networks: Neural networks or artificial neural networks (ANNs) are learning algorithms that depend on training data to learn. They are a subset of ML and structured to mimic how the human brain digests information and makes connections between different data points. Neural networks can keep learning as they process more data, improving their accuracy over time. On the flip side, you can shut off their learning and have them perform from what they’ve already learned.

Deep learning: Deep learning is a subfield of ANNs and refers to any neural network with three or more neuron layers. Deep learning algorithms are more powerful than shallower neural networks because of their enhanced learning abilities in optimizing and refining results for accuracy. It enables several AI applications from smart assistants (Think: Siri and Alexa) to other areas such as healthcare, fraud detection and facial recognition technologies.

Natural language processing: Natural language processing (NLP) is a field of AI that focuses on enabling computers to process human language. NLP models can do things such as translate from one language to another, summarize or classify text, and even generate language. This enables businesses to use AI to process customer experience data, sentiment analysis and more. It is also what powers conversational AIs like ChatGPT.

Computer vision: Computer vision is a sub-field of AI that’s focused on getting computers to efficiently process images for a number of use cases. One class of algorithms that can be applied here are convolutional neural networks (CNNs), which standout from other neural networks for their superior performance with image, speech, or audio signal inputs. Recent uses of deep learning and convolutional neural networks (CNNs) have led to breakthroughs in computer vision, enabling computers to process millions of image data and even create new images. CNNs are used in many applications such as early cancer detection, surveillance, space exploration and developing visual effects for films.

Though we are seeing continuous advancements in AI, more research is needed to explore its full potential. As it becomes a more integral part of our lives, responsible AI that considers privacy, security, transparency, fairness of intellectual property, reliability and inclusion is critical.

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]]> https://sproutsocial.com/glossary/tiktok-stories/ Thu, 07 Jul 2022 14:03:48 +0000 https://sproutsocial.com/?post_type=glossary_terms&p=162238/ What are TikTok stories? TikTok stories are TikTok videos that are deleted after 24 hours. If there’s a blue circle around a user’s profile Read more...

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]]> What are TikTok stories?

TikTok stories are TikTok videos that are deleted after 24 hours. If there’s a blue circle around a user’s profile picture, then that story is live. To check out a user’s story, click on their profile picture.

How to post a TikTok story

1. Make sure the most recent version of TikTok is on your device
2. Access your profile page
3. Tap the ‘+‘ button in the center. You can record content or upload videos and photos from your camera roll
4. Personalize your story with sounds and stickers
5. Publish the story to TikTok

 

 

 

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]]> https://sproutsocial.com/glossary/profile-picture/ Wed, 22 Jun 2022 18:13:49 +0000 https://sproutsocial.com/?post_type=glossary_terms&p=161737/ What is a profile picture? A profile pic is a photo that appears in your online accounts, regardless of whether it’s a social media Read more...

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]]> What is a profile picture?

A profile pic is a photo that appears in your online accounts, regardless of whether it’s a social media or professional site. Each social media site has its own unique way of displaying profile photographs as an avatar next to an account name.

Profile pictures are our own conscience representations in the eyes of the world. Psychologists find these photos fascinating because they illustrate how we view ourselves and how we desire to be seen by others.



What does a profile pic say about your personality?

While it’s common for us to select your profile photos at random, it’s not truly random. Instead of images, you share your personality on the internet. A look into your personality and emotional swings may be gained from what you share on social media. How someone is feeling, what they like, and how they act in their life may all be deduced from their profile picture.

If you want to know if someone is extroverted or introverted, conscientious or chaotic, open-minded or closed-minded, pleasant or disagreeable, neurotic or emotionally stable, you may look into their personality qualities as portrayed by their profile pic.

How can you use a profile picture to boost your social media profile?

First impressions are important when it comes to the internet. So, in the blink of an eye, you must make a strong impression to grab attention. The best way to do that is for your image to:

Have a clean background: If the background is too cluttered or intricate, you won’t be able to stand out.

Present your best self: Don’t use a profile picture with your friends, and don’t use a cropped image. Rather, use that photo of you from your previous vacation where you looked amazing.

Wear colorful clothes: Add a dash of color to your outfit so that you may communicate subconsciously with others, utilizing the color you showcase in your profile photo. Studies in psychology have shown that the color of your clothes can affect how people see you. Wearing red or hot pink will make you appear powerful or adoring, respectively. Wear emerald green jewelry to signify riches or progress.

Smile: Smiling is like getting a free facelift; it instantly builds a rapport. When you use it, you appear younger and more personable.

5 tips for creating perfect profile pictures for your social media

When it comes to social media, profile photographs play a significant impact. Accounts that do not have a profile picture are dubious. However, using photographs of poor quality is not a solution.

Take a look at the following technical tips for taking great profile pics:

1. Have a Mini Test Photo Shoot First: Move back and forth for the first few minutes of your photo shoot. Make sure you’re standing in the right spot, the composition looks good, and the picture is clear as a bell. Your eyes should be sharply focused on your picture.

2. Find a Well-Lit Area: Ideally, you want to find someplace with nice lighting, whether it’s in a park or a house. It will brighten your face, enhance your eye color, and illuminate your surroundings.

3. Avoid Hard Light: Your images will look squinty if you use harsh lighting. If you want to shoot self-portraits that are warm and well-lit, the sun setting is the time to do so.

4. Take Vertical Photos: Your clothes, stance, and position will all be better showcased in vertical shots. A vertical photo is preferable than a horizontal one for that.

5. Use Autofocus: Set your camera on autofocus so you don’t have to predict where you’re going to be. Self-portraits taken in areas with a lot of foregrounds should be handled with care. Your images may appear fuzzy if anything is blocking your camera’s lens.

The importance of a profile picture

Having a good profile pic is essential to your social life and networking online. It has an effect on your work prospects as well as your overall career.  Ready to step up your profile pic strategy? Check out our up-to-date guide on social media image sizes when creating your next profile pic.

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]]> https://sproutsocial.com/glossary/instagram-reels/ Thu, 16 Jun 2022 13:11:02 +0000 https://sproutsocial.com/?post_type=glossary_terms&p=161553/ Instagram announced Reels in August 2020, adding a popular feature from other top social media apps. Reels currently boasts over 500+ million users every day from all Read more...

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]]> Instagram announced Reels in August 2020, adding a popular feature from other top social media apps. Reels currently boasts over 500+ million users every day from all walks of life and across the planet.

People, influencers and brands all use Reels as an engaging and entertaining way to reach their friends and followers. Reels provides a near-endless supply of short video clips produced by everyday people and professionals alike.



What are Instagram Reels?

Instagram Reels are an alternative to TikTok and other micro-video sharing services. Reels started as fifteen-second videos but have since expanded its features to include a ninety-second video length. The app gives users high-end video editing tools and the ability to share their video with the world without needing a high-end computer.

A Reel can be created in the Instagram app or video can be imported from another source. The app has editing tools like Speed for making videos slower or faster, and Align to help create seamless transitions between shots. And with the built-in timer, video producers can be sure to capture what’s important.

Instagram provides a library of music licensed for use by Reels that people can choose and add to their video. Modern popular songs often find success first on video-sharing sites. Choosing the exact right fifteen seconds of music can be hard, but Reels makes it very easy to use a popular clip of music that the latest dance challenge videos feature.

How to use Instagram Reels

Instagram Reels is an excellent service for brands as well as everyday people. Brands can use Reels to market directly to their fans and customers. People like to watch entertaining videos, especially from brands they like. Nike gets an estimated 4.5 million views on an average short video shared with their fans.

With over 2 billion monthly users worldwide, Instagram is a very important market most social media manager’s can not afford to miss. Reels continue to grow in popularity every year and social media marketing needs to keep up.

Make your brands’ presence on the internet genuine by entertaining and helping those most interested in your message. A quick How-To guide about your service or showing your product in real-world scenarios are great examples of helping your viewers.

Short videos that are easy to create and share with many people are a great tool for spreading your message. Instagram Reels is a popular marketplace for engaging with friends, fans and customers. A well-made Reel creates value for the brand and the viewer.

Boost your Instagram engagement with Reels

A Reel is more than just a short video shared on a social media website. A Reel is a way for people to connect with other people and organizations in their life, to be entertained and informed. Reels are a modern form of mass communication, anyone can be popular for 15 seconds on Instagram.

Creating marketing content for the internet can be very time-consuming. Sprout Social offers many tools to help you with your social media projects. Check out our tips for Instagram Reels or try out a free 30 day trial.

 

 

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