What You Will Learn
- 1 Artificial Intelligence in Social Media
- 2 Natural Language Processing
- 3 What is the goal of Natural Language Processing?
- 4 What is the main challenge of natural language processing?
- 4.1 Context:
- 4.2 Synonyms:
- 4.3 Irony:
- 4.4 Variations:
- 4.5 Aspect mining
- 4.6 Categorization
- 4.7 Data enrichment
- 4.8 Data cleansing
- 4.9 Entity recognition
- 4.10 Intent recognition
- 4.11 Semantic analysis
- 4.12 Sentiment analysis
- 4.13 Syntax analysis
- 4.14 Taxonomy creation
- 4.15 Text summarization
- 4.16 The topic analysis
- 4.17 Learning:
- 5 NLP Framework
- 6 Conclusion
Artificial Intelligence in Social Media
Artificial Intelligence in social media holds the potential to transform how brands market across networks like Facebook, Instagram, Twitter, and LinkedIn. It can automate many tedious tasks related to social media management. And it can even do social media monitoring at scale. That might be why the “AI in social media” market is projected to grow from $633 million in 2018 to more than $2.1 billion by 2023, according to estimates from Markets and Markets. But what actually is artificial intelligence? How can Artificial Intelligence in social media actually impact your marketing and analytics? And, most importantly, how do you actually get started using artificial intelligence for social media?
Natural Language Processing
Natural language processing (NLP) is one of the fastest-growing areas of artificial intelligence (AI) and machine learning (ML). And it is on course to radically transform the way we interact with the digital world. The challenge in implementing natural language processing solutions lies in training algorithms to better understand the many nuances in natural speech. How can we teach a computer to understand things like irony, colloquialisms, and context? And, How can we train algorithms to recognize the myriad different accents out there? How will these solutions deal with errors and ambiguities? These are just some of the questions that make clear the need for extensive data labeling and training throughout the model development process.
For an NLP model to be useful in a real-world scenario, it needs to be trained using vast amounts of labeled data that has been prepared to the highest standards of accuracy and quality. But data labeling for machine learning is a demanding and time-consuming job, hence the value of working with supervised learning and data labeling service. This will help you scale your workforce, giving you time to focus on innovation.
What is the goal of Natural Language Processing?
The goals of NLP are two-fold: to facilitate the communication between people and machines in a more natural and user-friendly way, and to derive meaning and context from many hours of recorded speech or thousands of words of written content. While people speak and write in natural languages, computers rely on machine code to receive and carry out commands. That code is incomprehensible to most people, hence the rise of more user-friendly interface devices like keyboards, mice, and touchscreens. NLP goes a step further by providing a more direct and natural interface between people and computers.
However, NLP has a far wider range of implications than control alone. Every day, we speak and write thousands of words that others interpret to do countless things. NLP can also draw meaning from this speech and text, but at machine speed and on a scale that is impossible for people alone to even fathom. This is where NLP truly gains its value to determine things like context and sentiment across enormously broad audiences. This has profound significance in many applications, such as automated customer service and sentiment analysis for sales and marketing as well as brand reputation management.
What is the main challenge of natural language processing?
The biggest challenge of natural language processing is the complexity of natural language. While certain rules govern every natural language, there are many exceptions and ambiguities to those rules which, without the necessary understanding being applied to the model, can quickly derail its effectiveness. Here are some of the limitations and challenges the developers need to overcome:
Words and phrases often change their meaning depending on context. There are also homonyms to consider which, though pronounced and spelled the same, have completely different meanings in different To cater to this issue, NLP facilitates several solutions, such as introducing POS tagging or evaluating the context, although, understanding the semantic meaning of the words in a phrase remains an open task.
The most accurate NLP models need to understand not only synonyms but the subtle differences between them. For example, a sentiment analysis model might need to understand the difference in intensity between words like ‘good’ and ‘fantastic’.
Machines aren’t exactly known for creative use of speech or for making jokes, so it’s hardly surprising that irony and sarcasm present major challenges, which can lead to embarrassing misinterpretations.
There are around 160 dialects of English alone. On top of that are numerous regional colloquialisms and slang, along with domain-specific For example, an NLP designed for healthcare might not work as well with legal texts and recordings. You can use NLP to unlock hidden insights contained within the written text and verbal language, powering your algorithms and machine learning models. Let’s explore some of the common natural language processing techniques used for extracting information from text data:
is identifying aspects of language present in the text, such as part-of-speech
is placing text into organized groups and labeling it, based on features of interest. This is also known as text classification and text tagging.
is deriving and determining structure from text to enhance and augment the data. In an information retrieval case, expand a user’s query to enhance the matching probability of keywords is a form of
is establishing clarity on features of interest in the text by eliminating noise (distracting text) from the data to be analyzed. It involves multiple steps such as tackle punctuations, tokenization, and
is identifying text that represents specific entities, such as people, places, or organizations. This is also known as named entity recognition, entity chunking, and entity extraction.
is identifying words that signal a user’s intent, often used to determine actions to take based on users’ responses. This is also known as intent detection and intent classification.
analyzes the context and text structure to accurately distinguish the meaning of words with more than one definition. This is also called context analysis.
is extracting meaning from text to determine its emotion or sentiment. This is also called opinion mining.
is analyzing a string of symbols in the text, conforming to the rules of formal grammar. This is also known as syntactic analysis.
is creating a hierarchical structure of relationships among concepts in the text and specifying the terms to be used to refer to
is creating a brief description that includes the most important and relevant information contained in the text.
The topic analysis
is extracting meaning from text by identifying recurrent themes or topics. This is also known as topic labeling.
In general, there are three different levels at which sentiment analysis can be performed: the document level, sentence level, and aspect level. Sentiment analysis at the document level aims to identify the sentiments of users by analyzing the whole document. The sentence-level analysis is more fine-grained as the goal is to identify the polarity of sentences rather than the entire document. Aspect-level sentiment analysis focuses on identifying aspects or attributes expressed in reviews and classifying users’ opinions towards these aspects. Zemun KasZemun published his study as “Sentiment Analysis of Students’ Feedback with NLP and Deep
A Systematic Mapping Study” They considered several papers (27%) failed to report the information regarding the metrics used to assess the accuracy of their systems. Therefore, we consider that a special focus and emphasis should be placed on including the utilized metrics to enhance the transparency of the research results . “Sentiment analysis on Twitter tweets about COVID-19 vaccines using NLP and supervised KNN classification algorithm” illustrates general peoples’ sentiment towards the Pfizer, Moderna, and AstraZeneca vaccines made to fight COVIS-19.
During the pandemic, people under lockdown expressed their feelings on social media like Twitter about COVID- 19 and its vaccines. Therefore Twitter has become an important source of information. Extracting such tweets, the authors analyzed the sentiments of general people towards vaccines. Using NLP to preprocess the raw tweets and the KNN Classification Algorithm to classify the processed data. It is seen that general people have higher positive sentiment towards Pfizer and Moderna vaccine with the rate of 47.29 and 46.16 respectively compare to AstraZeneca vaccine with a rate of 40.08. 
“An NLP framework based on meaningful latent-topic detection and sentiment analysis via fuzzy lattice reasoning on youtube comments” proposed a hybrid framework that covers semantic and sentiment aspects by utilizing the advantage of Fuzzy lattice reasoning for detecting polarity, meaningful latent topics of the movie user comments on YouTube. In fact, they performed extensive experiments of YouTube comments about Oscar movies and demonstrated the strong dependencies between different kinds of sentiments expressed in comments and also detected the topic orientation of the discussed video contents. However, this study only considered the comments in the English language, and it requires to cover other languages too; besides, the user comments in this research are from the Oscar movies’ trailers and there is a need to consider the user comments from the full movies as well .
NLP is an interdisciplinary domain par excellence. It draws its sources from a large number of related disciplines like computer science, AI, linguistics, cognitive psychology, and neuroscience. Given this interdisciplinarity, the future of NLP will certainly be affected by developments in these connected domains. Natural Language Processing shows a strong domain for research. It has multiple areas and gaps that need to be improved.
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