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

The library enables developers to create applications that can process and understand massive volumes of text, and it is used to construct natural language understanding systems and information extraction systems. Sentiment analysis is a powerful technique that you can use to do things like analyze semantic analysis nlp customer feedback or monitor social media. With that said, sentiment analysis is highly complicated since it involves unstructured data and language variations. MonkeyLearn is a cloud-based text mining platform that helps businesses analyze text and visualize data using machine learning.

• Take the time to research and evaluate different options to find the right fit for your organization.
• The context of the document and relationships between words are preserved in the learned embedding.
• In semantic analysis, word sense disambiguation refers to an automated process of determining the sense or meaning of the word in a given context.
• I created a chatbot interface in a python notebook using a model that ensembles Doc2Vec and Latent Semantic Analysis(LSA).

Sentiment analysis is a Natural Language Processing (NLP) task concerned with opinions, attitudes, emotions, and feelings. 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.

SAP HANA Sentiment Analysis

Sentiment analysis refers to the process of using computation methods to identify and classify subjective emotions within a text. These emotions (neutral, positive, negative, and more) are quantified through sentiment scoring using natural language processing (NLP) techniques, and these scores are used for comparative studies and trend analysis. We chose MonkeyLearn as one of the top sentiment analysis tools because it helps businesses access real-time analysis with easy integrations from third-party apps. This platform also enables users to trigger actions and set up rules based on sentiments, such as escalating negative cases, prioritizing positive comments, or tagging tickets. MonkeyLearn’s workflow integrations provide a holistic view of customer sentiments gathered from various sources, resulting in rich insights and more actionable data. IBM Watson Natural Language Understanding (NLU) is an AI service for advanced text analytics that leverages deep learning to extract meaning and valuable insights from unstructured data.

SMOTE is an over-sampling approach in which the minority class is over-sampled by creating “synthetic” examples rather than by over-sampling with replacement. OK, the token length looks fine, and the tweet for maximum token length seems like a properly parsed tweet. I often mentor and help students at Springboard to learn essential skills around Data Science.

Three models were built to capture the content, sentiment, and contextual features of the data. Content features were extracted using Term Frequency/Inverse Document Frequency (TFIDF) to identify significant terms in each post. Sentiment features were derived from the grouping of second-person pronouns such as ‘you’, which could be used to form a harassment format. Contextual features were also included to distinguish between posts that had a harassment-like quality. The similarity of these features was then computed to detect potential cases of online harassment. Finally, a hybrid model was constructed by combining the three models and its performance was compared against the individual models.

Tokenization is the process of splitting a text into individual units, called tokens. Tokenization helps break down complex text into manageable pieces for further processing and analysis. Because BERT was trained on a large text corpus, it has a better ability to understand language and to learn variability in data patterns.

Top 15 sentiment analysis tools to consider in 2024

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. In this segment, we explore the landscape of Aspect Based Sentiment Analysis research, focusing on both individual tasks and integrated sub-tasks. We begin by delving into early research that highlights the application of graph neural network models in ABSA. This is followed by an examination of studies that leverage attention mechanisms and pre-trained language models, showcasing their impact and evolution in the field of ABSA. Many large companies are overwhelmed by the number of requests with varied topics.

Organizations can enhance customer understanding through sentiment analysis, which categorizes emotions into anger, contempt, fear, happiness, sadness, and surprise8. Moreover, sentiment analysis offers valuable insights into conflicting viewpoints, aiding in ChatGPT peaceful resolutions. It aids in examining public opinion on social media platforms, aiding companies and content producers in content creation and marketing strategies. It also helps individuals identify problem areas and respond to negative comments10.

It is used to derive intelligence from unstructured data for purposes such as customer experience analysis, brand intelligence and social sentiment analysis. For situations where the text to analyze is short, the PyTorch code library has a relatively simple EmbeddingBag class that can be used to create an effective NLP prediction model. The bag of Word (BOW) approach constructs a vector representation of a document based on the term frequency. However, a drawback of BOW representation is that word order is not preserved, resulting in losing the semantic associations between words. The representation vectors are sparse, with too many dimensions equal to the corpus vocabulary size31. Homonymy means the existence of two or more words with the same spelling or pronunciation but different meanings and origins.

Lexicon-based sentiment method predicts the sentiment using a built-in dictionary that has been given sentiment orientation. The sematic-based method makes predictions based on the evaluation of conceptual semantic and contextual semantics by co-occurrence patterns of words in a text. The semantic network and word clustering are the external semantic knowledge that aids the prediction of sentiment by the captured semantic relationship. Semantic networks represent the words to convey sentiment, while WordNet exploits the ontological structure. The comparison between supervised and lexicon-based procedures is tabulated in Table 4. Sexual harassment can be investigated using computation literary studies that the activities and patterns disclosed from large textual data.

This study employs sentence alignment to construct a parallel corpus based on five English translations of The Analects. Subsequently, this study applied Word2Vec, GloVe, and BERT to quantify the semantic similarities among these translations. The similarities and dissimilarities among these five translations were evaluated based on the resulting similarity scores.

Additionally, this research demonstrates the tangible benefits that Arabic sentiment analysis systems can derive from incorporating automatically translated English sentiment lexicons. Moreover, this study encompasses manual annotation studies designed to discern the reasons behind sentiment disparities between translations and source words or texts. This investigation is of particular significance as it contributes to the development of automatic translation systems. This research contributes to developing a state-of-the-art Arabic sentiment analysis system, creating a new dialectal Arabic sentiment lexicon, and establishing the first Arabic-English parallel corpus. Significantly, this corpus is independently annotated for sentiment by both Arabic and English speakers, thereby adding a valuable resource to the field of sentiment analysis.

Unlike frequency-based embeddings that focus on word occurrence statistics, prediction-based embeddings capture semantic relationships and contextual information, providing richer representations of word meanings. The process of creating word embeddings involves training a model on a large corpus of text (e.g., Wikipedia or Google News). The corpus is preprocessed by tokenizing the text into words, removing stop words and punctuation and performing other text-cleaning tasks.

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. Sentiment analysis tools determine the positive-negative polarity of user-generated text at their most basic level, and offer more advanced tools for working with larger datasets. The best sentiment analysis tools ensure accuracy in analyzing textual data and identify subtle emotions, sarcasm, and how a sentiment relates to the data. There are four key features to consider when selecting a sentiment analysis tool for your business. Another approach involves leveraging machine learning techniques to train sentiment analysis models on substantial quantities of data from the target language.

If you do not do that properly, you will suffer in the post-processing results phase. 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 application we will be building is a real-time chat application that is able to detect the tone of the users’ messages. As you can imagine the use cases for this can span greatly, from understanding customers’ interaction with customer service chats to understanding how well a production AI chatbot is performing. In part 1 we represented each review as a binary vector (1s and 0s) with a slot/column for every unique word in our corpus, where 1 represents that a given word was in the review.

Negative reviews have scores ≤ 4 out of 10 while a positive review ≥ 7 out of 10; neutral reviews are not included. This is expected, as these are the labels that are more prone to be affected by the limits of the threshold. Interestingly, ChatGPT tended to categorize most of these neutral sentences as positive.

Word embeddings are trained by exposing a model to a large amount of text data and adjusting the vector representations based on the context in which words appear. Two of the key selling points of SpaCy are that it features many pre-trained statistical models and word vectors, and has tokenization support for 49 languages. SpaCy is also preferred by many Python developers for its extremely high speeds, parsing efficiency, deep learning integration, convolutional neural network modeling, and named entity recognition capabilities. Moreover, many other deep learning strategies are introduced, including transfer learning, multi-task learning, reinforcement learning and multiple instance learning (MIL). Rutowski et al. made use of transfer learning to pre-train a model on an open dataset, and the results illustrated the effectiveness of pre-training140,141. Ghosh et al. developed a deep multi-task method142 that modeled emotion recognition as a primary task and depression detection as a secondary task.

Lexicon-based sentiment and emotion detection are applied to sentences containing instances of sexual harassment for data labelling and analysis. Finally, a long short-term memory-gated recurrent unit (LSTM-GRU) deep learning model is built to classify the sentiment characteristics that induce sexual harassment. The proposed model achieved an accuracy of 75.8% while outperforming five other algorithms. Additionally, a sentiment classification with three labels—negative, positive, and neutral—was developed using an LSTM-GRU RNN deep learning model.

Natural Language Toolkit (NLTK)

However, it is just the case that ChatGPT just couldn’t have guessed those ones. In sentence 5, it required knowledge of the situation at that moment in time to understand that the sentence represented a good outcome. And for sentence 8, knowledge is needed that an oil price drop correlates to a stock price drop for that specific target company. Ultimately, doing that for a total of 1633 (training + testing sets) sentences in the gold-standard dataset and you get the following results with ChatGPT API labels. Employee sentiment analysis is a specific application of sentiment analysis, which is an NLP technique designed to identify the emotional tone of a body of text. Sentiment analysis, also known as opinion mining, is widely used to detect how customers feel about products, brands and services.

Nonetheless, computational literary studies offer advantages such as quick interpretation, analysis, and prediction on extensive datasets (Kim and Klinger, 2018). Deep learning applies a variety of architectures capable of learning features that are internally detected during the training process. The recurrence connection in RNNs supports the model to memorize dependency information included in the sequence as context information in natural language tasks14. And hence, RNNs can account for words order within the sentence enabling preserving the context15.

But for the sake of simplicity, I’ll only demonstrate word vectorization (i.e tf-idf) here. As with any supervised learning task, the data is first divided into features (Feed) and label (Sentiment). Next, the data is split into train and test sets, and different classifiers are implemented starting with Logistic Regression. Precision, Recall, and F-score of the trained networks for the positive and negative categories are reported in Tables 10 and 11. The inspection of the networks performance using the hybrid dataset indicates that the positive recall reached 0.91 with the Bi-GRU and Bi-LSTM architectures. Considering the positive category the recall or sensitivity measures the network ability to discriminate the actual positive entries69.

Let’s run another optimization sweep, this time including a range of learning rates to test. Next we’ll create a PreProcessor object, containing methods for each of these steps, and run it on the text column of our data frame to tokenize, stem and remove stopwords from the tweets. Given a character sequence and a defined document unit, tokenization is the task of chopping it up into discrete pieces called tokens. In the process of chopping up text, tokenization also commonly involves throwing away certain characters, such as punctuation. Evaluating translated texts and analyzing their characteristics can be achieved through measuring their semantic similarities, using Word2Vec, GloVe, and BERT algorithms.

The startup’s summarization solution, DeepDelve, uses NLP to provide accurate and contextual answers to questions based on information from enterprise documents. Additionally, it supports search filters, multi-format documents, autocompletion, and voice search to assist employees in finding information. The startup’s other product, IntelliFAQ, finds answers quickly for frequently asked questions and features continuous learning to improve its results. These products save time for lawyers seeking information from large text databases and provide students with easy access to information from educational libraries and courseware. We will calculate the Chi square scores for all the features and visualize the top 20, here terms or words or N-grams are features, and positive and negative are two classes. Given a feature X, we can use Chi square test to evaluate its importance to distinguish the class.

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. Upon parsing, the analysis then proceeds to the interpretation step, which is critical for artificial intelligence algorithms.

You can foun additiona information about ai customer service and artificial intelligence and NLP. As a result, this sentence is categorized as containing sexual harassment content. Similarly, the second and third sentences also describe instances of sexual harassment. In these cases, the harasser exposes the victim to pornography and uses vulgar language to refer to them, resulting in unwanted sexual attention. On the other hand, the last three sentences contain sexual words but do not convey any sexual harassment content. For example, the keyword ‘fear’ is used to describe death, ‘porn’ refers to a career contextually unrelated to explicit material, and ‘destroy’ pertains to damaging dishes.

Random forest required more training time compared to other machine learning techniques. Conditional random field (CRF) is an undirected graphical model, and it has high performance on text and high dimensional data. CRF builds an observation sequence and is modelled based on conditional probability. ChatGPT App CRF is computationally complex in model training due to high data dimensionality, and the trained mode cannot work with unseen data. Semi-supervised is one type of supervised learning that leverages when there is a small portion of labelled with a large portion of unlabelled data.