Dissecting The Analects: an NLP-based exploration of semantic similarities and differences across English translations Humanities and Social Sciences Communications

The semantic analysis process begins by studying and analyzing the dictionary definitions and meanings of individual words also referred to as lexical semantics. Following this, the relationship between words in a sentence is examined to provide clear understanding of the context. The success of Word2Vec and GloVe have inspired further research into more sophisticated language representation models, such as FastText, BERT and GPT. These models leverage subword embeddings, attention mechanisms and transformers to effectively handle higher dimension embeddings.

Unsupervised learning methods to discover patterns from unlabeled data, such as clustering data55,104,105, or by using LDA topic model27. However, in most cases, we can apply these unsupervised models to extract additional features for developing supervised learning classifiers56,85,106,107. Doc2Vec is a neural network approach to learning semantic analysis nlp embeddings from a text document. Because of its architecture, this model considers context and semantics within the document. The context of the document and relationships between words are preserved in the learned embedding. The first step in the model is to identify the sentiment of each sentence from the chatbot message.

Latent Semantic Analysis & Sentiment Classification with Python

Sentiment analysis is a subset of AI, employing NLP and machine learning to automatically categorize a text and build models to understand the nuances of sentiment expressions. With AI, users can comprehend how customers perceive a certain product or service by converting human language into a form that machines can interpret. The output layer in a neural network generates the final network outputs based on the processing performed by the neurons in the previous layers. Several companies are using the sentiment analysis functionality to understand the voice of their customers, extract sentiments and emotions from text, and, in turn, derive actionable data from them. It helps capture the tone of customers when they post reviews and opinions on social media posts or company websites.

Given a sequence of words in a sentence, the CBOW model takes a fixed number of context words (words surrounding the target word) as input. Each context word is represented as an embedding (vector) through a shared embedding layer. Prediction-based embeddings can differentiate between synonyms and handle polysemy ChatGPT (multiple meanings of a word) more effectively. The vector space properties of prediction-based embeddings enable tasks like measuring word similarity and solving analogies. Prediction-based embeddings can also generalize well to unseen words or contexts, making them robust in handling out-of-vocabulary terms.

These natural language processing startups are hand-picked based on criteria such as founding year, location, funding raised, & more. Depending on your specific needs, your top picks might look entirely different. Constituent-based grammars are used to analyze and determine the constituents of a sentence.

Best Python Libraries for Sentiment Analysis

For example, semantic analysis can generate a repository of the most common customer inquiries and then decide how to address or respond to them. You can foun additiona information about ai customer service and artificial intelligence and NLP. Semantic analysis tech is highly beneficial for the customer service department of any company. Moreover, it is also helpful to customers as the technology enhances the overall customer experience at different levels. As far as limitations, Word2Vec may not effectively handle polysemy, where a single word has multiple meanings.

• The Bi-GRU-CNN model reported the highest performance on the BRAD test set, as shown in Table 8.
• This method provides a more holistic view of the model’s capabilities, accounting for variability and ensuring the robustness of the reported results.
• Luckily the dataset they provide for the competition is available to download.
• Comprehensive metrics and statistical breakdowns of these two datasets are thoughtfully compiled in a section of the paper designated as Table 2.

On the other hand, collocations are two or more words that often go together. Attention mechanisms and transformer models consider contextual information and bidirectional relationships between words, leading to more advanced language representations. Users can download pre-trained GloVe embeddings and fine-tune them for specific applications or use them directly. The CBOW model is trained by adjusting the weights of the embedding layer based on its ability to predict the target word accurately. The aggregated representation is then used to predict the target word using a softmax activation function.

The most notable feature of PyNLPl is its comprehensive library for developing Format for Linguistic Annotation (FoLiA) XML. TextBlob is a Python (2 and 3) library that is used to process textual data, with a primary focus on making common text-processing functions accessible via easy-to-use interfaces. Objects within TextBlob can be used as Python strings that can deliver NLP functionality to help build text analysis applications.

• It considers how frequently words co-occur with each other in the entire dataset rather than just in the local context of individual words.
• Another reason behind the sentiment complexity of a text is to express different emotions about different aspects of the subject so that one could not grasp the general sentiment of the text.
• We can see that the spread of sentiment polarity is much higher in sports and world as compared to technology where a lot of the articles seem to be having a negative polarity.

Furthermore, dataset balancing occurs after preprocessing but before model training and evaluation41. As a result, balancing the dataset in deep learning leads to improved model performance and reduced overfitting. Therefore, the datasets have up-sampled the positive and neutral classes and down-sampled the negative class via the SMOTE sampling technique.

Accuracy serves as a measure of the proportion of correct predictions out of the total predictions made by the model. Precision and recall provide more nuanced evaluations of classification models. Precision represents the ratio of true positive predictions to all predicted positive instances, while recall denotes the ratio of true positive predictions to all actual positive instances.

Data Cleaning

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. I specifically defined k_neighbors as 1 for this toy data, since there are only two entries of negative class, if SMOTE chooses one to copy, then only one other negative entry left as a neighbour. Compared to the model built with original imbalanced data, now the model behaves in opposite way. 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.

F1 is a composite metric that combines precision and recall using their harmonic mean. In the context of classifying sexual harassment types, accuracy can be considered as the primary performance metric due to the balanced sample size and binary nature of this classification task. Additionally, precision, recall, and F1 can be utilized as supplementary metrics to support and provide further insights into model performance. As shown in Table 14, Logistic regression (LR) gained higher accuracy in compared to other algorithms. Azure AI language’s state-of-the-art natural language processing capabilities including Z-Code++ and Azure OpenAI Service is powered by breakthrough AI research.

That means that if we average over all the words, the effect of meaningful words will be reduced by the glue words. Please note that we should ensure that all positive_concepts and negative_concepts are represented in our word2vec model. But the characteristic of low precision and high recall is as same as oversampled data. 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.

Next, significant NLP preprocessing operations are carried out to enhance our classification model and carry out an experiment on DL algorithms. In this paper, classification is performed using deep learning algorithms, especially RNNs such as LSTM, GRU, Bi-LSTM, and Hybrid algorithms (CNN-Bi-LSTM). During model building, different parameters were tested, and the model with the smallest loss or error rate was selected. Therefore, we conducted different experiments using different deep-learning algorithms.

The negative precision or the true negative accuracy, which estimates the ratio of the predicted negative samples that are really negative, reported 0.91 with the Bi-GRU architecture. Table 8a, b display the high-frequency words and phrases observed in sentence pairs with semantic similarity scores below 80%, after comparing the results from the five translations. This set of words, such as “gentleman” and “virtue,” can convey specific meanings independently.

And people usually tend to focus more on machine learning or statistical learning. The importance of customer sentiment extends to what positive or negative sentiment the customer expresses, not just directly to the organization, but to other customers as well. People commonly share their feelings about a brand’s products or services, whether they are positive or negative, on social media. If a customer likes or dislikes a product or service that a brand offers, they may post a comment about it — and those comments can add up. Such posts amount to a snapshot of customer experience that is, in many ways, more accurate than what a customer survey can obtain.

What are the types of NLP categories?

Python is an extremely efficient programming language when compared to other mainstream languages, and it is a great choice for beginners thanks to its English-like commands and syntax. Another one of the best aspects of the Python programming language is that it consists ChatGPT App of a huge amount of open-source libraries, which make it useful for a wide range of tasks. SST will continue to be the go-to dataset for sentiment analysis for many years to come, and it is certainly one of the most influential NLP datasets to be published.

LSA is a Bag of Words(BoW) approach, meaning that the order (context) of the words used are not taken into account. However, I have seen many BoW approaches outperform more complex deep learning methods in practice, so LSA should still be tested and considered as a viable approach. The model consists of two document embeddings, one from LSA and the other from Doc2Vev. To train the LSA and Doc2Vec models, I concatenated perfume descriptions, reviews, and notes into one document per perfume. I then use cosine similarity to find perfumes that are similar to the positive and neutral sentences from the chatbot message query. I remove recommendations of perfumes that are similar to the negative sentences.

Social media users express their opinions using different languages, but the proposed study considers only English language texts. To solve this limitation future researchers can design bilingual or multilingual sentiment analysis models. Social media websites are gaining very big popularity among people of different ages. Platforms such as Twitter, Facebook, YouTube, and Snapchat allow people to express their ideas, opinions, comments, and thoughts.

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. Then, benchmark sentiment performance against competitors and identify emerging threats. Continuous updates ensure the hybrid model improves over time, enhancing its ability to accurately reflect customer opinions. There’s no singular best NLP software, as the effectiveness of a tool can vary depending on the specific use case and requirements.

The motivation behind this research stems from the arduous task of creating these tools and resources for every language, a process that demands substantial human effort. This limitation significantly hampers the development and implementation of language-specific sentiment analysis techniques similar to those used in English. The critical components of sentiment analysis include labelled corpora and sentiment lexica.