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

The code for Pytorch is significantly longer than the code required for Keras. If you prefer to write code quickly and not spell out every training step, then Keras is a better option for you. However, if you want to understand everything that happens during training, Pytorch makes this possible. For a step-by-step guide to Pytorch with examples, check out this introductory post. For a cool project with Pytorch, I recommend this great tutorial by Venelin Valkov, where he shows you how to use BERT with Huggingface transformers and Pytorch, and then deploy that model with FASTAPI.

Sentiment scores range from  − 1 to + 1, with  − 1 indicating very negative article content and + 1 the opposite. This paper tackles the challenge of using social media content, especially Twitter, for emergency response use during disasters. We explore mechanisms for identifying and ranking the most relevant tweets related to a specific search term. We use hurricane Irma as a use case and demonstrate methods for identifying relevant tweets by optimizing different parameters.

Its semantic role labelling model is based on BERT and boasts 86.49 test F1 on the Ontonotes 5.0 dataset (Shi & Lin, 2019). Sentiment analysis has been extensively studied at different granularities (e.g., ChatGPT App document-level, sentence-level and aspect-level) in the literature. At the document level, the goal is to detect the sentiment polarity of an entire review, which may be composed of multiple sentences.

Mean cosine similarity of tweet terms in vector vocabulary (MCS)

Texts generated by humans in social media sites contain lots of noise that can significantly affect the results of the sentiment classification process. Moreover, depending on the features generation approach, every new term seems to add at least one new dimension to the feature space. This article is devoted to binary sentiment analysis using the Naive Bayes classifier with multinomial distribution. We go through the brief overview of constructing a classifier from the probability model, then move to data preprocessing, training and hyperparameters optimization stages. Moving onward from rule-based approaches, the next method attempted is a logistic regression — among the most commonly used supervised learning algorithms for classification. Logistic regression is a linear model trained on labelled data — the term linear is important because it means the algorithm only uses linear combinations (i.e. sums and not products) of inputs and parameters to produce a class prediction.

Next, I will choose two sets of words that hold positive and negative sentiments expressed commonly in the movie review context. You can foun additiona information about ai customer service and artificial intelligence and NLP. Then, to predict the sentiment of a review, we will calculate the text’s similarity in the word embedding ChatGPT space to these positive and negative sets and see which sentiment the text is closest to. This research studies the impact of online news on social and economic consumer perceptions through semantic network analysis.

Before determining employee sentiment, an organization must find a way to collect employee data. The organization first sends out open-ended surveys that employees can answer in their own words. Then NLP tools review each answer, analyzing the sentiment behind the words and providing a detailed report to managers and HR.

The next most useful feature selected by Chi-square test is “great”, I assume it is from mostly the positive reviews. Sentiment analysis tools are essential to detect and understand customer feelings. Companies that use these tools to understand how customers feel can use it to improve CX. Companies can use customer sentiment to alert service representatives when the customer is upset and enable them to reprioritize the issue and respond with empathy, as described in the customer service use case.

With these upgraded features, you can access the highest accuracy scores in the field of natural language processing. Furthermore, many details in the research process have much room for further improvement. Additional features, such as indices for contextual semantic characteristics and the number of argument structure nestifications, could be included in the analysis. Moreover, the current study does not involve the refinement of semantic analysis tools since the modification and improvement of language models require high technique level and a massive quantity of training materials. Nonetheless, it is imperative for further studies to enhance these models and tools for semantic labelling and analysis, so as to promote a deeper understanding of semantic structures across different text types and languages. Sentiments are then aggregated to determine the overall sentiment of a brand, product, or campaign.

Monitor your long-term brand health

In line with past research, e.g.62,63, we dynamically selected the number of lags using the Bayesian Information Criteria. The models indicate that 61% of the semantic importance series of ERKs Granger-cause the Personal component of the Consumer Climate index, while only 34% Granger-cause the Future component and 27% the Current component. It is not surprising that average consumers have a better understanding of their personal situation when responding to questions but may be less informed about economic cycles. When answering questions about their own financial situation, individuals are likely to have a more accurate understanding of their personal circumstances. However, when it comes to broader economic trends and cycles, the average consumer may not have the same level of knowledge or expertise.

There are several existing algorithms you can use to perform the topic modeling. The most common are Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) In this section I give anoverview of the techniques without getting into technical details. Let’s compare our zero-shot text classification model with the state-of-the-art models and random pick in micro-average F1. Actually, the latest implementations of zero-shot text classification born out of a very simple but brilliant idea.

By understanding semantic nuances and related terms, the company can provide more accurate search results and personalized product recommendations, leading to increased sales and customer loyalty. Cdiscount, an online retailer of goods and services, uses semantic analysis to analyze and understand online customer reviews. When a user purchases an item on the ecommerce site, semantic analysis example they can potentially give post-purchase feedback for their activity. This allows Cdiscount to focus on improving by studying consumer reviews and detecting their satisfaction or dissatisfaction with the company’s products. In semantic analysis, word sense disambiguation refers to an automated process of determining the sense or meaning of the word in a given context.

This approach makes it easy to mine for opinions about the Ukrainian conflict, to get an idea of what people think about it, and how hopeful/fearful they are. To achieve this goal, the top 50 “hot” posts of six different subreddits about Ukraine and news (Ukraine, worldnews, Ukraina, UkrainianConflict, UkraineWarVideoReport, and UkraineWarReports) and their relative comments are scraped to create a novel data set. We employed various important analyzes on this corpus to promote the use of a dictionary approach, which scores the hopefulness of every submitted user post. Finally, we performed a topic modeling analysis using the Latent Dirichlet Allocation (LDA) algorithm to understand the main issues that are raised by users and what are the key talking points. This article proposes a novel lexicon-based unsupervised sentiment analysis method to measure the “hope” and “fear” for the 2022 Ukrainian-Russian Conflict.

If those outputs passed through a data pipeline, and if a sentiment model did not go through a proper bias detection process, the results could be detrimental to future business decisions and tarnish a company’s integrity and reputation. Your business could end up discriminating against prospective employees, customers, and clients simply because they fall into a category — such as gender identity — that your AI/ML has tagged as unfavorable. Talkwalker has a simple and clean dashboard that helps users monitor social media conversations about a new product, marketing campaign, brand reputation, and more. It offers a quick brand overview that includes KPIs for engagement, volume, sentiment, demographics, and geography. Users can also access graphs for real-time trends and compare multiple brands to easily benchmark against competitors. Customers benefit from such a support system as they receive timely and accurate responses on the issues raised by them.

Danmaku domain lexicon construction based on MIBE neologism recognition algorithm

By identifying patterns and trends in machine performance, the company can proactively schedule maintenance and minimize downtime, improving operational efficiency and reducing costs. LLMs can automatically classify and categorize content based on predefined criteria, streamlining content management processes and enhancing data organization. This automated classification enables the most effective information retrieval methods, paving the way for knowledge discovery within organizations. Google incorporated ‘semantic analysis’ into its framework by developing its tool to understand and improve user searches. The Hummingbird algorithm was formed in 2013 and helps analyze user intentions as and when they use the google search engine.

Variation in Minimum Word Frequency also affected the maximums for each scalar comparison formula differently. With each of the other parameters, the maximum AU-ROC score consistently correlated with the same value for all scalar comparison formulas (e.g. the optimal value for Word Window Size, 8, corresponded to a maximum AU-ROC for all four formulas, see Table 4). With Minimum Word Frequency, the optimal value for three of the four formulas was 8.

These results suggest that a significant portion of the selected keywords can be used to predict changes in the Climate dimension, providing valuable insights for future research and decision-making. Our tests indicate that a higher number of keywords could impact how consumers perceive the Future situation. However, the most significant impact appears to be on the personal climate, as evidenced by 61% of significant Granger causality tests.

Semantic subsumption

Deep learning and word embeddings further improved accuracy scores for sentiment analysis. In 2013, Google created the Word2Vec embedding algorithm, which along with the GloVe algorithm remains the two most popular word embedding methods. For a practical walk-through, check out this post, where the author uses embeddings to create a book recommendation system. Traditionally, for deep learning classification a word embedding would be used as part of a recurrent or convolutional neural network. However, these networks take a very long time to train, because with recurrence and convolutions it is difficult to parallelize.

This feature helps you quickly identify and respond to various types of feedback, which gives you context on how to engage with your audience. Here’s an example of positive sentiment from one of Girlfriend Collective’s product pages. Track conversations and social mentions about your brand across social media, such as X, Instagram, Facebook and LinkedIn, even if your brand isn’t directly tagged. Doing so is a great way to capitalize on praise and address criticism quickly.

Sentiment analysis is most effective when you’re able to separate your positive mentions from your negative mentions. This involves identifying sentiment-indicative terms within these mentions and categorizing them as positive, negative‌ or neutral. Tools like Sprout can help facilitate this process by allowing you to monitor mentions, keywords and hashtags related to your brand and industry. This helps you stay informed about trending topics, competitors and complementary products. Monitoring these sentiments allows you to understand the overall perception of your brand. Sentiment analysis can improve the efficiency and effectiveness of support centers by analyzing the sentiment of support tickets as they come in.

For this process, after tokenization and cleaning, each remaining token, \(\tau _i\), in each tweet was scored based upon its cosine similarity to the seed term irma. If a term was not present in the vocabulary, due to minimum word count or other restricting criteria, the term was given a zero, which evaluates to a neutral context relation due to cosine similarity. The mean of all cosine similarity values for tokens \(\tau\) within the tweet, including zeroes, was calculated, and this value was designated as the score for the tweet. It has been observed within the vector constructs for Word2Vec that vector operations, such as addition and subtraction, yield meaning10,26. This was used as the predicate for interpreting the meaning of a tweet as the sum of its component word vectors.

The final result is displayed in the plot below, which shows how the accuracy (y-axis) changes for both models when categorizing the numeric Gold-Standard dataset, as the threshold (x-axis) is adjusted. Also, the training and testing sets are on the left and right sides, respectively. Recall that linear classifiers tend to work well on very sparse datasets (like the one we have). Another algorithm that can produce great results with a quick training time are Support Vector Machines with a linear kernel. So far we’ve chosen to represent each review as a very sparse vector (lots of zeros!) with a slot for every unique n-gram in the corpus (minus n-grams that appear too often or not often enough). Linear classifiers typically perform better than other algorithms on data that is represented in this way.

However, in real scenarios, there may not be sufficient labeled training data, and even if provided with sufficient training data, the distributions of training data and target data are almost certainly different to some extent. Employee sentiment analysis enables HR to more easily and effectively obtain useful insights about what employees think about the organization by analyzing how they communicate in their work environment. This lets HR keep a close eye on employee language, tone and interests in email communications and other channels, helping to determine if workers are happy or dissatisfied with their role in the company.

This method, however, is not very effective as it is almost impossible to think of all the relevant keywords and their variants that represent a particular concept. CSS on the other hand just takes the name of the concept (Price) as input and filters all the contextually similar even where the obvious variants of the concept keyword are not mentioned. Yet even though the context is not about ranking because of the sentiment, some SEOs will quote this kind of research and then tack on that it’s being used for ranking. And that’s wrong because the context of this and other research papers are consistently about understanding text, well outside of the context of ranking that text. This article investigates the antecedents of consumer confidence by analyzing the importance of economic-related keywords as reported on online news. After mining online Italian news over a period of four years, we found that most of the selected keywords impact how consumers perceive their personal economic situation.

Therefore, we propose to use DNNs to extract implicit sentiment features. Furthermore, to better adapt a pre-trained model to downstream tasks, some researchers proposed to design new pre-training tasks28,32. For instance, the work of SentiBERT designed specific pre-training tasks to guide a model to predict phrase-level sentiment label32.

Zero-shot classification models are versatile and can generalize across a broad array of sentiments without needing labeled data or prior training. Further studies are needed to explore whether similar distinction exists in other language pairs, especially those having a higher level of similarity in information structures. In the above example, the verb in the source text is “been”, but the predicate is changed to the verb “下滑(decline)” in the translation, which comes from the word “slide” in the source text. Transformation in predicates of this kind, known as denominalization, is essentially one of the major factors contributing to the difference in semantic depths of verbs. Through denominalization in the translation process, the notion of “decline” is reintroduced to the predicate verb, which eliminates the incongruency between the lexico-grammatical and semantic layers, resulting in more explicit information.

Common examples of root cause analysis in manufacturing include methodologies such as the Fishbone diagram. To perform RCA using machine learning, we need to be able to detect that something is out of the ordinary, or in other words, that an anomaly or an outlier is present. Content analytics is an NLP-driven approach to cluster videos (e.g. youTube) into relevant topics based on the user comments.

• Through denominalization in the translation process, the notion of “decline” is reintroduced to the predicate verb, which eliminates the incongruency between the lexico-grammatical and semantic layers, resulting in more explicit information.
• At the time, he was developing sophisticated applications for creating, editing and viewing connected data.
• Substantial evidence for syntactic-semantic explicitation, simplification, and levelling out is found in CT, validating that translation universals are found not only at the lexical and grammatical levels but also at the syntactic-semantic level.
• After training, the Word2Vec neural network produces vectors for terms but not tweets.
• Because when a document contains different people’s opinions on a single product or opinions of the reviewer on various products, the classification models can not correctly predict the general sentiment of the document.

The raw data with phrase-based fine-grained sentiment labels is in the form of a tree structure, designed to help train a Recursive Neural Tensor Network (RNTN) from their 2015 paper. The component phrases were constructed by parsing each sentence using the Stanford parser (section 3 in the paper) and creating a recursive tree structure as shown in the below image. A deep neural network was then trained on the tree structure of each sentence to classify the sentiment of each phrase to obtain a cumulative sentiment of the entire sentence.

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. Here are a couple examples of how a sentiment analysis model performed compared to a zero-shot model. The other major effect lies in the conversion and addition of certain semantic roles for logical explicitation.

If a word is not found in the GloVe dictionary, the word embedding values for the word are zero. The algorithm forms a prediction based on the current behavioral pattern of the anomaly. If the predicted values exceed the threshold confirmed during the training phase, an alert is sent. A simplified form of this is commonly taught to school-age children, in the identification of words as nouns, verbs, adjectives, adverbs etc. TF-IDF weight is a statistical measure used to evaluate how important a word is to a document in a collection or corpus. The importance increases proportionally to the number of times a word appears in the document but is offset by the frequency of the word in the corpus.

As an emerging user-generated comment, the danmaku has its unique emotional and content characteristics compared to traditional comment data, and needs to be combined with the video content to analyze the potential meaning between the lines7. Aiming at the new features of danmakus, scholars have carried out explorations and attempts of sentiment analysis. In recent years, with the development of neural networks, more scholars apply deep learning methods in the danmaku sentiment analysis tasks. The neural network and machine learning methods without using pre-trained models performed the worst, with the overall performance far lower than the methods using pre-trained models.

Paired with other semantically relevant or topically rich content on your web page, the purpose and meaning of your web content is unambiguously clear to search engines. With these advancements, Google can look at a piece of content and understand not only the topic it covers, but the related subtopics, terms, and entities and how all of those various concepts interrelate. We’ve gone over several options for transforming text that can improve the accuracy of an NLP model. Which combination of these techniques will yield the best results will depend on the task, data representation, and algorithms you choose. It’s always a good idea to try out many different combinations to see what works. We can observe that the features with a high χ2 can be considered relevant for the sentiment classes we are analyzing.

These papers focus largely on the use of social media as “sensors”, where individuals on the ground during crisis events can be leveraged to provide information. These individuals are not necessarily official responders, yet their information can be reliable when properly processed. While this paper agrees with the assessments of this work, it seeks to expand upon their research and provide a possible method for parsing social media information in a rapidly changing context. We illustrate the efficacy of GML by the examples from CR as shown in Table 5 and Figure 7. On \(t_1\), both GML and the deep learning model give the correct label; however, on all the other examples, GML gives the correct labels while the deep learning model mispredicts. In Figure 7, the four subfigures show the constructed factor subgraphs of the examples respectively.