Leveraging on NLP to gain insights in Social Media, News & Broadcasting by George Regkas

Semantic analysis allows organizations to interpret the meaning of the text and extract critical information from unstructured data. Semantic-enhanced machine learning tools are vital natural language processing components that boost decision-making and improve the overall customer experience. Semantic analysis refers to a process of understanding natural language (text) by extracting insightful information such as context, emotions, and sentiments from unstructured data. It gives computers and systems the ability to understand, interpret, and derive meanings from sentences, paragraphs, reports, registers, files, or any document of a similar kind. Keras, a Python-based deep learning library, was developed to enable fast experimentation and ease of use for building and training deep neural networks. It works as an interface for the machine learning platforms TensorFlow and Theano.

Some of the library’s other top use cases include finding text similarity and converting words and documents to vectors. Topping our list is Natural Language Toolkit (NLTK), which is widely considered the best Python library for NLP. NLTK is an essential library that supports tasks like classification, tagging, stemming, parsing, and semantic reasoning. It is often chosen by beginners looking to get involved in the fields of NLP and machine learning.

NLTK also provides access to more than 50 corpora (large collections of text) and lexicons for use in natural language processing projects. Hugging Face is known for its user-friendliness, allowing both beginners and advanced users to use powerful AI models without having to deep-dive into the weeds of machine learning. Its extensive model hub provides access to thousands of community-contributed models, including those fine-tuned for specific use cases like sentiment analysis and question answering. Hugging Face also supports integration with the popular TensorFlow and PyTorch frameworks, bringing even more flexibility to building and deploying custom models. Hugging Face Transformers has established itself as a key player in the natural language processing field, offering an extensive library of pre-trained models that cater to a range of tasks, from text generation to question-answering.

Specifically, Google recently introduced Bidirectional Encoded Representations of Transformers (BERT), a transformer architecture that serves as an English language model trained on a corpus of over 800 million words in the general domain13. BERT encodes bidirectional representations of text using self-supervision, allowing for rich embeddings that capture meaning in human language (i.e., syntax and semantics). A classification (CLS) feature vector is an output from the last layer of the BERT model representing the embedding that captures syntactic and semantic information from the input text, which can be used to train additional ML models such as a classifier13. Importantly, BERT can be easily adapted to new domains by transfer learning with minimal fine-tuning, providing an ideal language model for specialized domains such as medicine13,17,18. In addition to predicting the onset of psychosis, the methods provide insight into the thought processes affected in the emergence of psychosis. Our results from randomly shuffling words showed that semantic density was a function of the way words were organized into sentences, not simply which words were used across sentences.

Network settings

A document can not be processed in its raw format, and hence it has to be transformed into a machine-understandable representation27. Selecting the convenient representation scheme suits the application is a substantial step28. The fundamental methodologies used to represent text data as vectors are Vector Space Model (VSM) and neural network-based representation. Text components are represented by numerical vectors which may represent a character, word, paragraph, or the whole document. Also, Gensim includes several kinds of algorithms such as LDA, RP, LSA, TF-IDF, hierarchical Dirichlet processes (HDPs), LSI, and singular value decomposition (SVD). Hence, all the mentioned algorithms are unsupervised, so there is no need for human input or training corpus.

Each dimension consists of two poles corresponding to a pair of adjectives with opposite semantics (i.e., antonym pairs). The position interval between the poles of each dimension is divided into seven equally-sized parts. Then, given the object, respondents are asked to choose one of the seven parts in each dimension. The closer the position is to a pole, the closer the respondent believes the object is semantically related to the corresponding adjective. Before covering Latent Semantic Analysis, it is important to understand what a “topic” even means in NLP. Today the CMSWire community consists of over 5 million influential customer experience, customer service and digital experience leaders, the majority of whom are based in North America and employed by medium to large organizations.

The core idea is to take a matrix of what we have — documents and terms — and decompose it into a separate document-topic matrix and a topic-term matrix. The bag-of-words model is commonly used in methods of document classification where the (frequency of) occurrence of each word is used as a feature for training a classifier. The horizontal axis in this figure represents the time axis, measured in months. Meanwhile, the vertical ChatGPT App axis indicates the event selection similarity between Ukrainian media and media from other countries. Each circle represents a country, with the font inside it representing the corresponding country’s abbreviation (see details in Supplementary Information Tab.S3). The size of a circle corresponds to the average event selection similarity between the media of a specific country and the media of all other countries.

Finally, each translated English text was aligned with its corresponding original text. One can train machines to make near-accurate predictions by providing text samples as input to semantically-enhanced ML algorithms. Machine learning-based semantic analysis involves sub-tasks such as relationship extraction and word sense disambiguation. The semantic analysis method begins with a language-independent step of analyzing the set of words in the text to understand their meanings. This step is termed ‘lexical semantics‘ and refers to fetching the dictionary definition for the words in the text.

The matrices 𝐴𝑖 are said to be separable because they can be decomposed into the outer product of two vectors, weighted by the singular value 𝝈i. Calculating the outer product of two vectors with shapes (m,) and (n,) would give us a matrix with a shape (m,n). In other words, every possible product of any two numbers in the two vectors is computed and placed in the new matrix.

KEA was developed based on the work of Turney (2002) and was programmed in the Java language; it is a simple and efficient two-step algorithm that can be used across numerous platforms (Frank et al., 1999). You can track sentiment over time, prevent crises from escalating by prioritizing mentions with negative sentiment, compare sentiment with competitors and analyze reactions to campaigns. One of the tool’s features is tagging the sentiment in posts as ‘negative, ‘question’ or ‘order’ so brands can sort through conversations, and plan and prioritize their responses.

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All in all, semantic analysis enables chatbots to focus on user needs and address their queries in lesser time and lower cost. For example, semantic analysis can generate a repository of the most common customer inquiries and then decide how to address or respond to them. Relationship extraction is a procedure used to determine the semantic relationship between words in a text. In semantic analysis, relationships include various entities, such as an individual’s name, place, company, designation, etc. Moreover, semantic categories such as, ‘is the chairman of,’ ‘main branch located a’’, ‘stays at,’ and others connect the above entities.

MXNet is an open-source deep learning framework used for training and deploying artificial neural networks. It is designed to scale from large clusters of GPUs to multiple machines, and it supports various programming languages such as Python, R, Scala, and Julia. MXNet provides automatic differentiation, a crucial feature for training deep learning models, enabling the computation of gradients based on the model’s parameters. Released under Apache License 2.0, Deeplearning4j (DL4J) is an open-source, distributed deep learning library written for Java and Java Virtual Machine (JVM) languages. DL4J includes implementations of various deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and more.

It allows users to automatically generate unique and human-like copy in seconds, and best of all it supports over 100 languages. Copy.ai is designed for SEO professionals, when creating a post you can simply choose your title, keywords, the desired tone of the writing, and the goal of the article (such as teaching). AI Writing – Utilize the full power of Surfer to write well-researched and high-quality articles. Keywords Volume & Search Intent – Check search intent for your target audience and evaluate monthly search volume and keyword difficulty at a glance. While Google does offer this functionality for free via the Google Keyword Planner, this tool is easier and less frustrating to use.

Toolkits for Topic Models

Australian startup Servicely develops Sofi, an AI-powered self-service automation software solution. Its self-learning AI engine uses plain English to observe and add to its knowledge, which improves its efficiency over time. This allows Sofi to provide employees and customers with more accurate ChatGPT information. The flexible low-code, virtual assistant suggests the next best actions for service desk agents and greatly reduces call-handling costs. We’re just starting to feel the impact of entity-based search in the SERPs as Google is slow to understand the meaning of individual entities.

Some sentiment analysis tools can also analyze video content and identify expressions by using facial and object recognition technology. A sentiment analysis tool uses artificial intelligence (AI) to analyze textual data and pick up on the emotions people are expressing, like joy, frustration or disappointment. In this post, you’ll find some of the best sentiment analysis tools to help you monitor and analyze customer sentiment around your brand. Decoding those emotions and understanding how customers truly feel about your brand is what sentiment analysis is all about. One of the top selling points of Polyglot is that it supports extensive multilingual applications. According to its documentation, it supports sentiment analysis for 136 languages.

The five translators examined in this study have effectively achieved a balance between being faithful to the original text and being easy for readers to accept by utilizing apt vocabulary and providing essential para-textual information. As English translations of The Analects continue to evolve, future translators can further enhance this work by summarizing and supplementing paratextual information, thereby building on the foundations established by their predecessors. By integrating insights from previous translators and leveraging paratextual information, future translators can provide more precise and comprehensive explanations of core concepts and personal names, thus enriching readers’ understanding of these terms. Within the similarity score intervals of 80–85% and 85–90%, the distributions of sentences across all five translators is more balanced, each accounting for about 20%.

Semantic analysis plays a vital role in the automated handling of customer grievances, managing customer support tickets, and dealing with chats and direct messages via chatbots or call bots, among other tasks. Users can create long-form blog posts in minutes from a keyword, YouTube video, podcast, existing blog, PDF or document, or custom audio file – all with your own unique voice and writing style. For SEO-focused content publishers who need long-form content, and with the ability to product content quickly it is a solid option. Designed for SEO, and for websites that need to scale content, the generative AI model is designed to generate humanlike content and passes even the strongest and most accurate AI detectors. Create – Write SEO content that ranks by using the most advanced versions of NLP and NLU (Natural Language Processing & Natural Language Understanding). It offers real-time optimization based on SERP statistics, and offers content that can deliver.

We have reached a stage in AI technologies where human cognition and machines are co-evolving with the vast amount of information and language being processed and presented to humans by NLP algorithms. Understanding the co-evolution of NLP technologies with society through the lens of human-computer interaction can help evaluate the causal factors behind how human and machine decision-making processes work. Identifying the causal factors of bias and unfairness would be the first step in avoiding disparate impacts and mitigating biases. Focusing specifically on social media platforms, these tools are designed to analyze sentiment expressed in tweets, posts and comments. They help businesses better understand their social media presence and how their audience feels about their brand. One of the pre-trained models is a sentiment analysis model trained on an IMDB dataset, and it’s simple to load and make predictions.

Visualize – If you need to add art to your words, this brings your character sheets and worldbuilding documents to life with art generated from your descriptions. Artificial intelligence (AI) technologies have come a long way in a short amount of time. These technologies were traditionally limited to tasks that were clearly laid out with guidelines. As a final exercise, let’s see what results we get when we train the embeddings with the same number of dimensions as the GloVe data. The target classes are strings which need to be converted into numeric vectors. This is done with the LabelEncoder from Sklearn and the to_categorical method from Keras.

Moreover, it also plays a crucial role in offering SEO benefits to the company. Semantic analysis techniques and tools allow automated text classification or tickets, freeing the concerned staff from mundane and repetitive tasks. In the larger context, this enables agents to focus on the prioritization of urgent matters and deal with them on an immediate basis. It also shortens response time considerably, which keeps customers satisfied and happy. There are countless applications of NLP, including customer feedback analysis, customer service automation, automatic language translation, academic research, disease prediction or prevention and augmented business analytics, to name a few. While NLP helps humans and computers communicate, it’s not without its challenges.

We read in the CSV file with the tweets and apply a random shuffle on its indexes. Before we can use words in a classifier, we need to convert them into numbers. Each tweet could then be represented as a vector with a dimension equal to (a limited set of) the words in the corpus.

Closing out our list of 10 best Python libraries for NLP is PyTorch, an open-source library created by Facebook’s AI research team in 2016. The name of the library is derived from Torch, which is a deep learning framework written in the Lua programming language. Originally a third-party extension to the SciPy library, semantic analysis in nlp scikit-learn is now a standalone Python library on Github. It is utilized by big companies like Spotify, and there are many benefits to using it. For one, it is highly useful for classical machine learning algorithms, such as those for spam detection, image recognition, prediction-making, and customer segmentation.

• For the DCT task we used the original stories to calculate the a priori vectors.
• The startup’s reinforcement learning-based recommender system utilizes an experience-based approach that adapts to individual needs and future interactions with its users.
• In Section Proposed Topic Modeling Methodology, we focus on five TM methods proposed in our study besides our evaluation process and its results.
• It allows users to build custom ML models using AutoML Natural Language, a tool designed to create high-quality models without requiring extensive knowledge in machine learning, using Google’s NLP technology.
• TM is a machine learning method that is used to discover hidden thematic structures in extensive collections of documents (Gerrish and Blei, 2011).
• The search query we used was based on four sets of keywords shown in Table 1.

More than 8 million event records and 1.2 million news articles are collected to conduct this study. The findings indicate that media bias is highly regional and sensitive to popular events at the time, such as the Russia-Ukraine conflict. Furthermore, the results reveal some notable phenomena of media bias among multiple U.S. news outlets. While they exhibit diverse biases on different topics, some stereotypes are common, such as gender bias. This framework will be instrumental in helping people have a clearer insight into media bias and then fight against it to create a more fair and objective news environment.

Data Cleaning

You can foun additiona information about ai customer service and artificial intelligence and NLP. For context, topic modeling is a technique used to discover hidden thematic structures in large collections of text documents. Gensim allows you to analyze, compare, and interpret large collections of textual data by enabling the creation of high-quality semantic representations. PyTorch is an open source machine learning (ML) framework based on Python and Torch library and is used for building deep learning models such as computer vision and natural language processing. It was originally developed by Meta AI, but it’s currently part of the Linux Foundation. The PyTorch ecosystem includes many high-level APIs and tools that simplify tasks like data loading, natural language processing, and reinforcement learning.

• This is done with the LabelEncoder from Sklearn and the to_categorical method from Keras.
• Analysis reveals that core concepts, and personal names substantially shape the semantic portrayal in the translations.
• The columns and rows we’re discarding from our tables are shown as hashed rectangles in Figure 6.
• As you can see, if the Tf-Idf values for both original data are 0, then synthetic data also has 0 for those features, such as “adore”, “cactus”, “cats”, because if two values are the same there are no random values between them.
• Due to the massive influx of unstructured data in the form of these documents, we are in need of an automated way to analyze these large volumes of text.

The average values for all measures per group are shown as average ‘speech profiles’ (spider plots) in Fig. 1B, C we show speech profiles for two participants’ descriptions of one of the TAT pictures. For the TAT task, we used a priori descriptions of each of the 8 pictures from [39]; see Section S1. For the DCT task we used the original stories to calculate the a priori vectors. Note that we did not obtain tangentiality scores from free speech, due to the absence of an a priori description.

Video recordings of SIPS interviews for all the 40 participants were transcribed by the same research member, who was blind to the conversion status. Semantic density and content analyses started with a series of pre-processing stages. First, speech from participants was separated from speech produced by interviewers. Second, individual sentences and part-of-speech (POS) categories were identified. This was accomplished using the Stanford Probabilistic Context-Free Grammar (PCFG) parser,45 with the maximum length of the sentence set to 60 words. In addition to applying POS tags to individual words (e.g., nouns, verbs, adjectives, adverbs, determiners, and pronouns), the Stanford Parser was able to tokenize sentences, that is, automatically identify all the sentences in a string of text.

H2O.ai is a fully open source, distributed in-memory machine learning platform that supports widely used statistical & machine learning algorithms, including gradient boosted machines, generalized linear models, and deep learning. Our study was approved by the Hamilton Integrated Research Ethics Board, study protocol 7766-C. As this study was a retrospective chart review, it was approved by the REB with waiver of consent. We collected 11,418 historical synopses for bone marrow specimens spanning April 2001 to December 2019. Due to the format’s limitation, the synopsis structure was lost and fields were mixed with descriptions. In addition, noise (i.e., irrelevant information) including signatures from doctors and the reporting system’s context were included in the text.

The fore cells handle the input from start to end, and the back cells process the input from end to start. The two layers work in reverse directions, enabling to keep the context of both the previous and the following words47,48. Stanford CoreNLP is a library consisting of a variety of human language technology tools that help with the application of linguistic analysis tools to a piece of text. CoreNLP enables you to extract a wide range of text properties, such as named-entity recognition, part-of-speech tagging, and more with just a few lines of code. In addition to the interpretation of search queries and content, MUM and BERT opened the door to allow a knowledge database such as the Knowledge Graph to grow at scale, thus advancing semantic search at Google. Natural language processing will play the most important role for Google in identifying entities and their meanings, making it possible to extract knowledge from unstructured data.

The precisions for the negative class are around 47~49%, but the recalls are way higher at 64~67%. So from our set of data we got a lot of texts classified as negative, many of them were in the set of actual negative, however, a lot of them were also non-negative. The last entry added by RandomOverSampler is exactly same as the fourth one (index number 3) from the top. RandomOverSampler simply repeats some entries of the minority class to balance the data. If we look at the target sentiments after RandomOverSampler, we can see that it has now a perfect balance between classes by adding on more entry of negative class.

Along with services, it also improves the overall experience of the riders and drivers.

Word embeddings play a significant role in shaping the information sphere and can aid in making consequential inferences about individuals. Job interviews, university admissions, essay scores, content moderation, and many more decision-making processes that we might not be aware of increasingly depend on these NLP models. Today, semantic analysis methods are extensively used by language translators. Earlier, tools such as Google translate were suitable for word-to-word translations. However, with the advancement of natural language processing and deep learning, translator tools can determine a user’s intent and the meaning of input words, sentences, and context.