Forecasting consumer confidence through semantic network analysis of online news Scientific Reports

Now let’s see how such a model performs (The code includes both OSSA and TopSSA approaches, but only the latter will be explored). Please note that we should ensure that all positive_concepts and negative_concepts are represented in our word2vec model. For all statistical models reported in the paper, we have checked that the data meet the assumptions of the model, primarily via posterior predictive checks and visualization. Note that our main modeling approach relies on a generalized linear model, which does not have formal tests for many assumptions. Aside from being an effective literary device, social thinkers have long suggested that the use of passive language, and more generally, non-agentive language, is strongly related to the degree to which people possess personal agency6,7,8,9,10. Control has long been considered a fundamental need (i.e., “der Wille zur Macht”, translated as the “will to power” in the philosophy of Nietzche13) and much research suggests that it is crucial for people’s well-being12,14,15,16,17,18.

Originating from the adaptation of Convolutional Neural Networks (CNNs) to graph data84,85, the MLEGCN enhances this model by introducing mechanisms that capture complex relational dynamics within sentences. 10, the distribution of compound scores is different between the two types of sexual harassment. Most of the sentences with physical sexual harassment content has nearly a maximum of negative sentiment. You can foun additiona information about ai customer service and artificial intelligence and NLP. This gives the insights that the physical sexual harassment may be impactfully to the effect the sentiment negatively compared to the non-physical sexual harassment. The first layer of LSTM-GRU is an embedding layer with m number of vocab and n output dimension. The next layer is LSTM with 128 units, it produces a significant feature sequence as the input of the GRU layer.

ChatGPT Prompts for Text Analysis

In general, the procedure of exploring data to collect valuable information is stated as text mining. Text mining includes data mining algorithms, NLP, machine learning, and statistical operations ChatGPT to derive useful content from unstructured formats such as social media textual data. Hence, text mining can improve commercial trends and activities by extracting information from UGC.

In recent years, most of the data in every sphere of our lives have become digitized, and as a result, there is a need for providing powerful tools and methods to deal with this existing digital data increase in order to understand it. Indeed, there have been many developments in the NLP domain, including rule-based systems and statistical NLP approaches, that are based on machine learning algorithms for text mining, information extraction, sentiment analysis, etc. Some typical NLP real-world applications currently in use include automatically summarizing documents, named entity recognition, topic extraction, relationship extraction, spam filters, TM, and more (Farzindar and Inkpen, 2015). In the areas of information retrieval and text mining, such as the TM method, several methods perform keyword and topic extraction (Hussey et al., 2012). TM is a machine learning method that is used to discover hidden thematic structures in extensive collections of documents (Gerrish and Blei, 2011). The development of embedding to represent text has played a crucial role in advancing natural language processing (NLP) and machine learning (ML) applications.

Research methodology

From Table 8, the trained model registers accuracy, precision and recall of 99%, while the model performs poorly during validation and testing on the given unseen datasets. This shows the model is memorizing the training data instead of learning, which resulted in over-fitting. Sentiment analysis, also known as Opinion mining, is the study of people’s attitudes and sentiments about products, services, and their attributes4. ChatGPT App Sentiment analysis holds paramount importance in political discourse, particularly within the Amharic-speaking region of Ethiopia5. Instances from global and local political landscapes underscore the impact of sentiment analysis on political reform. For instance, the 2008 election of Barack Obama in the United States showed the role of social media in shaping political sentiment, galvanizing support, and mobilizing voters.

• This evaluation entails employing multiple translation tools or engaging multiple human translators to cross-reference translations, thereby facilitating the identification of potential inconsistencies or discrepancies.
• There are only nearly 0.1% of sentences (570 out of 58,458) are detected as containing sexual harassment-related words.
• It indicates that topics extracted from news could be used as a signal to predict the direction of market volatility the next day.
• Originally, the algorithm is said to have had a total of five different phases for reduction of inflections to their stems, where each phase has its own set of rules.

This 15-dimensional vector will be used later as a feature vector for a classification problem, to assess whether topics obtained on a certain day can be used to predict the direction of market volatility the next day. The most commonly-used method for topic modeling, or topic discovery from a large number of documents, is Latent Dirichlet allocation (LDA). LDA is a generative topic model which generates combination of latent topics from a collection of documents, where each combination of topics produces words from the collection’s vocabulary with certain probabilities.

Sentiment analysis datasets

More recently, various attention-based neural networks have been proposed to capture fine-grained sentiment features more accurately24,25,26. Unfortunately, these models are not sufficiently deep, and thus have only limited efficacy for polarity detection. A comparative study was conducted applying multiple deep learning models based on word and character features37. Three CNN and five RNN networks were implemented and compared on thirteen reviews datasets. Although the thirteen datasets included reviews, the deep models performance varied according to the domain and the characteristics of the dataset.

EHRs often contain several different data types, including patients’ profile information, medications, diagnosis history, images. In addition, most EHRs related to mental illness include clinical notes written in narrative form29. Therefore, it is appropriate to use NLP techniques to assist in disease diagnosis on EHRs datasets, such as suicide screening30, depressive disorder identification31, and mental condition prediction32. The use of social media has become increasingly popular for people to express their emotions and thoughts20. In addition, people with mental illness often share their mental states or discuss mental health issues with others through these platforms by posting text messages, photos, videos and other links. Prominent social media platforms are Twitter, Reddit, Tumblr, Chinese microblogs, and other online forums.

After preprocessing and converting the datasets to a format that can be analyzed, the words in the sentence must be represented as vectors so that Word2Vec can calculate similarity, analogy. The embedding layer converts the input into an \(N\times M\) dimensional vector, where N represents the longest sentence in the dataset and M represents the embedding dimension. Some authors recently explored with code-mixed language to identify sentiments and offensive contents in the text. Similar results were obtained using ULMFiT trained on all four datasets, with TRAI scoring the highest at 70%. For the identical assignment, BERT trained on TRAI received a competitive score of 69%.

Notably, sentiment analysis algorithms trained on extensive amounts of data from the target language demonstrate enhanced proficiency in detecting and analyzing specific features in the text. Another potential approach involves using explicitly trained machine learning models to identify and classify these features and assign them as positive, negative, or neutral sentiments. These models can subsequently be employed to classify the sentiment conveyed within the text by incorporating slang, colloquial language, irony, or sarcasm.

Top Natural Language Processing Software Comparison

Regrettably, the exploration of translation universals from such a perspective is relatively sparse. One is the lack of automated semantic analytical methods for large-scale corpora. Despite the growth semantic analysis of text of corpus size, research in this area has proceeded for decades on manually created semantic resources, which has been labour-intensive and often confined to narrow domains (Màrquez et al., 2008).

Sentiment Analysis is a Natural Language Processing field that increasingly attracts researchers, government authorities, business owners, service providers, and companies to improve products, services, and research. Therefore, research on sentiment analysis of YouTube comments related to military events is limited, as current studies focus on different platforms and topics, making understanding public opinion challenging. As a result, we used deep learning techniques to design and develop a YouTube user sentiment analysis of the Hamas-Israel war.

Word embeddings contribute to the success of question answering systems by enhancing the understanding of the context in which questions are posed and answers are found. BERT is the most accurate of the four libraries discussed in this post, but it is also the most computationally expensive. SpaCy is a good choice for tasks where performance and scalability are important. TextBlob is a good choice for beginners and non-experts, while NLTK is a good choice for tasks where efficiency and ease of use are important.

When I started delving into the world of data science, even I was overwhelmed by the challenges in analyzing and modeling on text data. However, after working as a Data Scientist on several challenging problems around NLP over the years, I’ve noticed certain interesting aspects, including techniques, strategies and workflows which can be leveraged to solve a wide variety of problems. I have covered several topics around NLP in my books “Text Analytics with Python” (I’m writing a revised version of this soon) and “Practical Machine Learning with Python”. In the current study, We leveraged data from online discussion forums that provide a space for people living with depression to investigate the hypothesis that people who experience depression increase their use of non-agentive language.

Among these, we only considered the encoding of the [CLS] token to represent the news article, as it captures BERT’s understanding at the news level. In particular, this model was based on a neural network that processed encodings extracted by a pre-trained BERT model. In the following, the encodings extraction stage is first detailed, and then the neural network structure and its optimization are described. Lastly, we calculated the language sentiment of all articles as a control variable and a possible additional predictor of the Consumer Confidence Index and its dimensions. Sentiment was computed using the SBS BI web app45, which uses a lexicon similar to VADER55 for the Italian language. Sentiment scores range from  − 1 to + 1, with  − 1 indicating very negative article content and + 1 the opposite.