A computational text analysis investigation of the relation between personal and linguistic agency Communications Psychology

Text summarization, semantic search, and multilingual language models expand the use cases of NLP into academics, content creation, and so on. The cost and resource-efficient development of NLP solutions is also a necessary requirement to increase their adoption. VeracityAI is a Ghana-based startup specializing in product design, development, and prototyping using AI, ML, and deep learning. The startup’s reinforcement learning-based recommender system utilizes an experience-based approach that adapts to individual needs and future interactions with its users.

Besides, it provides summaries of audio content within a few seconds and supports multiple languages. SummarizeBot’s platform thus finds applications in academics, content creation, and scientific research, among others. Python is the perfect programming language for developing text analysis applications, due to the abundance of custom libraries available that are focused on delivering natural language processing functions. Now that we have an understanding of what natural language processing can achieve and the purpose of Python NLP libraries, let’s take a look at some of the best options that are currently available. NLP is a type of artificial intelligence that can understand the semantics and connotations of human languages, while effectively identifying any usable information. This acquired information — and any insights gathered — can then be used to build effective data models for a range of purposes.

In this section, we look at how to load and perform predictions on the trained model. And single qubit states \(\left| \psi _a\right\rangle\) and \(\left| \psi _b\right\rangle\) represent marginal cognitive models of text perceived through isolated conceptual distinctions A and B. Same phenomena can be described in information terms such that action potentials are considered as signals linking binary neural registers while total activity of the nervous system is referred to as psyche, cognition or mind51,52. In traditional psychology, activity of the mind is described verbally as dynamics of ideas, thoughts, motives, emotions, etc.36,53. Overall, the samples from the English periodical were rather more subdued in tone than those in Spanish with regard to economic expectations in the period before the pandemic, but emotional activity is almost identical in the two periodicals. I often mentor and help students at Springboard to learn essential skills around Data Science.

Vectara is a US-based startup that offers a neural search-as-a-service platform to extract and index information. It contains a cloud-native, API-driven, ML-based semantic search pipeline, Vectara Neural Rank, that uses large language models to gain a deeper understanding of questions. Moreover, Vectara’s semantic search requires no retraining, tuning, stop words, synonyms, knowledge graphs, or ontology management, unlike other platforms. SpaCy can be used for the preprocessing of text in deep learning environments, building systems that understand natural language and for the creation of information extraction systems. There are other types of texts written for specific experiments, as well as narrative texts that are not published on social media platforms, which we classify as narrative writing.

Feature extraction: polarity relations

These results can be improved further by training the model for additional epochs with text preprocessing steps that includes oversampling and undersampling of the minority and majority classes, respectively10. Now-A-days, using the internet to communicate with others and to obtain information is necessary and usual process. The majority of people may now use social media to broaden their interactions and connections worldwide. Persons can express any sentiment about anything uploaded by people on social media sites like Facebook, YouTube, and Twitter in any language. Pattern recognition and machine learning methods have recently been utilized in most of the Natural Language Processing (NLP) applications1.

Since it has been widely recognized that BERT-based pre-trained models can capture sentimental features more accurately than manually crafted features (e.g., sentiment lexicons), we leverage labeled training data to extract sentiment features by BERT-based models. Similar to the existing DNN models, it trains a sentence-level polarity classifier such that the sentences with similar polarities can be clustered within local neighborhood in a deep embedding space. ChatGPT App To enable knowledge conveyance beyond local neighborhood, we also separately train a semantic network to extract implicit polarity relations between two arbitrary sentences. All the extracted features are then modeled as binary factors in a factor graph to fulfill gradual learning. In the example, given the evidential observations and the binary similarity factors, the labels of \(t_3\), \(t_1\) and \(t_2\) can be subsequently reasoned to be negative.

RACL-BERT also showed significant performance in certain tasks, likely benefiting from the advanced contextual understanding provided by BERT embeddings. The TS model, while not topping any category, showed consistent performance across tasks, suggesting its robustness. The same dataset, which has about 60,000 sentences with the label of highest-scored ChatGPT emotion, is used to train the emotion classification. The sequential model is built, and its architecture of the model is demonstrated in Fig. The model starts with a Glove word embedding as the embedding layer and is followed by the LSTM and GRU layers. There is a dropout layer was added for LSTM and GRU, respectively, to reduce the complexity.

Looks like the most negative article is all about a recent smartphone scam in India and the most positive article is about a contest to get married in a self-driving shuttle. We can get a good idea of general sentiment statistics across different news categories. Looks like the average sentiment is very positive in sports and reasonably negative in technology! The following code computes sentiment for all our news articles and shows summary statistics of general sentiment per news category. Spacy had two types of English dependency parsers based on what language models you use, you can find more details here.

Word embeddings are a way of representing words as vectors in a multi-dimensional space, where the distance and direction between vectors reflect the similarity and relationships among the corresponding words. Sprout Social helps you understand and reach your audience, engage your community and measure performance with the only all-in-one social media management platform built for connection. Medallia’s experience management platform offers powerful listening features that can pinpoint sentiment in text, speech and even video. Run the model on one piece of text first to understand what the model returns and how you want to shape it for your dataset.

Neural basis of quantum cognitive modeling

Urdu, on the other hand, is a resource-poor language with a severe lack of sentiment lexicon. Problems with Urdu word segmentation, morphological structure and vocabulary variances are among the main deterrents to developing a fully effective Urdu sentiment analysis model. The simple Python library supports complex analysis and operations on textual data. For lexicon-based approaches, TextBlob defines a sentiment by its semantic orientation and the intensity of each word in a sentence, which requires a pre-defined dictionary classifying negative and positive words. The tool assigns individual scores to all the words, and a final sentiment is calculated. Another top option for sentiment analysis is VADER (Valence Aware Dictionary and sEntiment Reasoner), which is a rule/lexicon-based, open-source sentiment analyzer pre-built library within NLTK.

TM has been applied to numerous areas of study such as Information Retrieval, computational linguistics and NLP. Also, it has been effectively applied to clustering, querying, and retrieval tasks for data sources such as text, images, video, and genetics. TM approaches still have challenges related to methods used to solve real-world tasks like scalability problems.

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. VADER is a lexicon and rule-based sentiment analysis tool that is tuned to capture sentiments expressed in social media. Gilbert in 2014, but since then it underwent several improvements and updates. The VADER sentiment analyzer is extremely accurate when it comes to social media texts because it provides not only positive/negative scores but also a numeric measure of the intensity of the sentiment. Another advantage of using VADER is that it does not need training data as it uses human labeled sentiment lexicon and works fairly fast even on simple laptops. One of the main advantages of using these models is their high accuracy and performance in sentiment analysis tasks, especially for social media data such as Twitter.

• Section “Results” showcases the primary findings, subsequently analyzed in Section “Discussion and conclusions”.
• Figures 14 and 15 show the changes in values when we compare the two periods in the Spanish and English periodicals, respectively.
• If the experiment is performed, the system transfers to one of the superposed potential outcomes according to probabilities \(p_i\).
• The first objective was to assess the overall translation quality using the BLEU algorithm as a benchmark.
• All the comparative experiments have been conducted on the same machine, which runs the Ubuntu 16.04 operating system and has a NVIDIA GeForce RTX 3090 GPU, 128 GB of memory and 2 TB of solid-state drive.

A dropout layer is followed by the LSTM to reduce the complexity of the ensemble model. A dense layer with 16 neurons is added to overcome the sparsity of GRU’s output. An output layer which is the 3 neurons dense layer, is added for sentiment classification, and 5 neurons dense layer is added for emotion semantic analysis of text detection, respectively. The loss function of ‘categorical_crossentropy’ and the ‘adam optimizer’ is used for training. Table 9 presents the sentences that have been labelled as containing sexually harassing words, along with the corresponding keywords detected through a rule-based approach.

Early identification and resolution of emerging issues show your brand’s commitment to quality and customer care. These tools allow you to conduct thorough social sentiment analytics, which can help you refine your brand messaging, engage more effectively with customers, monitor your brand’s long-term health and identify emerging issues with your products or services. The Active Listeners tab provides one-click access to queries, including complaints, compliments and specific customer experiences. This feature helps you quickly identify and respond to various types of feedback, which gives you context on how to engage with your audience.

Conclusions and future work

Three sarcasm identification corpora containing tweets, quote responses, news headlines were used for evaluation. The proposed representation integrated word embedding, weighting functions, and N-gram techniques. The weighted representation of a document was computed as the concatenation of the weighted unigram, bigram and trigram representations.

The limitations of Boosting and Bagging are the computational expensive and lack of interpretability. Logistic regression is a statistical model based on a decision boundary to predict the probability of labels. Naïve Bayes classification is popular in document categorization and information retrieval. This model used the frequency of the words in the document and based on Bayes theorem to predict the probability of the models. The limitation of Naïve Bayes models is the modal has a strong assumption on the distribution of data that must obey on Bayes theorem. K-nearest neighbours (KNN) algorithm predicts the class based on the similarity of the test document and the k number of the nearest document.

Deep neural architectures have proved to be efficient feature learners, but they rely on intensive computations and large datasets. In the proposed work, LSTM, GRU, Bi-LSTM, Bi-GRU, and CNN were investigated in Arabic sentiment polarity detection. The applied models showed a high ability to detect features from the user-generated text.

However, challenges remain, such as handling negation and exploring n-grams for improved feature sets. Sentiment analysis is a process in Natural Language Processing that involves detecting and classifying emotions in texts. The emotion is focused on a specific thing, an object, an incident, or an individual. Although some tasks are concerned with detecting the existence of emotion in text, others are concerned with finding the polarities of the text, which is classified as positive, negative, or neutral. The task of determining whether a comment contains inappropriate text that affects either individual or group is called offensive language identification. The existing research has concentrated more on sentiment analysis and offensive language identification in a monolingual data set than code-mixed data.

This is thanks to Python’s many libraries that have been built specifically for NLP. But the characteristic of low precision and high recall is as same as oversampled data. The last entry added by RandomOverSampler is exactly same as the fourth one (index number 3) from the top. You can foun additiona information about ai customer service and artificial intelligence and NLP. 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. Random over-sampling is simply a process of repeating some samples of the minority class and balance the number of samples between classes in the dataset.

Accuracy obtained is an approximation of the neural network model’s overall accuracy23. In addition, deep models based on a single architecture (LSTM, GRU, Bi-LSTM, and Bi-GRU) are also investigated. The datasets utilized to validate the applied architectures are a combined hybrid dataset and the Arabic book review corpus (BRAD). Tables 6 and 7 presents the obtained results using various machine learning techniques with different features on our proposed UCSA-21 corpus. The results reveal that SVM performance is slightly better on the UCSA-21 dataset than other machine learning algorithms, with an accuracy of 72.71% using combination (1-2) features.

With the growth of online social network platforms and applications, large amounts of textual user-generated content are created daily in the form of comments, reviews, and short-text messages. As a result, users often find it challenging to discover useful information or more on the topic being discussed from such content. Machine learning and natural language processing algorithms are used to analyze the massive amount of textual social media data available online, including topic modeling techniques that have gained popularity in recent years. This paper investigates the topic modeling subject and its common application areas, methods, and tools. Also, we examine and compare five frequently used topic modeling methods, as applied to short textual social data, to show their benefits practically in detecting important topics. These methods are latent semantic analysis, latent Dirichlet allocation, non-negative matrix factorization, random projection, and principal component analysis.

It applies NLP techniques for identifying and detecting personal information from opinionated text. Sentiment analysis deduces the author’s perspective regarding a topic and classifies the attitude polarity as positive, negative, or neutral. In the meantime, deep architectures applied to NLP reported a noticeable breakthrough in performance compared to traditional approaches. The outstanding performance of deep architectures is related to their capability to disclose, differentiate and discriminate features captured from large datasets.

Although for both the high sentiment complexity group and the low subjectivity group, the S3 does not necessarily fall around the decision boundary, yet -for different reasons- it is harder for our model to predict their sentiment correctly. Traditional classification models cannot differentiate between these two groups, but our approach provides this extra information. The following two interactive plots let you explore the reviews by hovering over them. Each review has been placed on the plane in the below scatter plot based on its PSS and NSS. Therefore, all points above the decision boundary (diagonal blue line) have positive S3 and are then predicted to have a positive sentiment, and all points below the boundary have negative S3 and are thus predicted to have a negative sentiment.