This paper covers the two approaches for sentiment analysis: i) lexicon based method; ii) machine learning method. We describe several techniques to implement these approaches and discuss how they can be adopted for sentiment classification of Twitter messages. We present a comparative study of different lexicon combin…
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48 results for “Lexicon Features”
In lexicon-based classification, documents are assigned labels by comparing the number of words that appear from two opposed lexicons, such as positive and negative sentiment. Creating such words lists is often easier than labeling instances, and they can be debugged by non-experts if classification performance is unsa…
Paper introduces methods to integrate external knowledge into RNNs using attention mechanisms.
problem Incorporating external knowledge into RNNs for improved performance.
method Proposes three methods: attentional concatenation, feature-based gating, and affine transformation.
result Attentional feature-based gating consistently improves performance across tasks.
The use of robo-readers to analyze news texts is an emerging technology trend in computational finance. In recent research, a substantial effort has been invested to develop sophisticated financial polarity-lexicons that can be used to investigate how financial sentiments relate to future company performance. However, …
Existing methods for CWS usually rely on a large number of labeled sentences to train word segmentation models, which are expensive and time-consuming to annotate. Luckily, the unlabeled data is usually easy to collect and many high-quality Chinese lexicons are off-the-shelf, both of which can provide useful informatio…
We introduce a novel discriminative latent variable model for bilingual lexicon induction. Our model combines the bipartite matching dictionary prior of Haghighi et al. (2008) with a representation-based approach (Artetxe et al., 2017). To train the model, we derive an efficient Viterbi EM algorithm. We provide empiric…
In this paper we consider two sequence tagging tasks for medieval Latin: part-of-speech tagging and lemmatization. These are both basic, yet foundational preprocessing steps in applications such as text re-use detection. Nevertheless, they are generally complicated by the considerable orthographic variation which is ty…
The paper predicts TSE stocks using social media sentiment and volume.
problem Predicting Tehran Stock Exchange (TSE) variables using social media data.
method Hybrid sentiment analysis combining lexicon-based and learning-based methods; built a sentiment lexicon for Persian language.
result Sentiment and volume of online comments are useful for predicting TSE stocks.
This paper describes the resource- and system-building efforts of an eight-week Johns Hopkins University Human Language Technology Center of Excellence Summer Camp for Applied Language Exploration (SCALE-2009) on Semantically-Informed Machine Translation (SIMT). We describe a new modality/negation (MN) annotation schem…
Simple framework decouples word alignment and multilingual embedding mapping.
problem Learning multilingual embeddings without supervision.
method Two-stage approach: 1) unsupervised word alignment, 2) mapping embeddings to shared space.
result Robust performance across various multilingual tasks, including distant languages.
Paper refines cross-lingual word embeddings using Manhattan norm.
problem Sensitivity of norm loss function to outliers in CLWEs.
method Post-processing step using norm to improve CLWEs.
result The refinement substantially outperforms state-of-the-art baselines.
Hybrid engine analyzes news sentiment for markets in real-time.
problem Real-time market analysis of news sentiment.
method Three-way ensemble learning combining financial lexicon, adaptive TF-IDF clustering, and auto-calibrated weighting.
result Adaptive statistical clustering learner improves adaptability to market changes.
For decades, context-dependent phonemes have been the dominant sub-word unit for conventional acoustic modeling systems. This status quo has begun to be challenged recently by end-to-end models which seek to combine acoustic, pronunciation, and language model components into a single neural network. Such systems, which…
Study compares BERT with other sentiment analysis models.
problem Comparing sentiment analysis techniques.
method Used four models: Sent WordNet, logistic regression, LSTM, and BERT on IMDB movie reviews.
result BERT outperformed other models in sentiment classification.
MIOpen optimizes deep learning operators for GPUs, accelerating research.
problem Optimizing deep learning operators for efficient GPU execution.
method Highly optimized implementations, fusion, auto-tuning, bfloat16 support.
result Accelerates time to discovery in deep learning research.
A new Twitter sentiment model predicts stock market trends with high accuracy.
problem Real-time prediction of future stock market prices.
method Baseline correlation approach using polynomial regression, classification, and lexicon-based sentiment analysis.
result Predicts stock market trends with 67.22% accuracy, up to 15 time samples in advance.
The starting point of this article is the question "How to retrieve fingerprints of rhythm in written texts?" We address this problem in the case of Brazilian and European Portuguese. These two dialects of Modern Portuguese share the same lexicon and most of the sentences they produce are superficially identical. Yet t…
This paper discusses the effect of hubness in zero-shot learning, when ridge regression is used to find a mapping between the example space to the label space. Contrary to the existing approach, which attempts to find a mapping from the example space to the label space, we show that mapping labels into the example spac…
FinLlama uses a fine-tuned Llama 2 model for financial sentiment analysis.
problem Accurate financial sentiment analysis for better trading decisions.
method Fine-tuning Llama 2 7B model on financial sentiment data, using a generator-classifier scheme.
result FinLlama provides nuanced insights into financial news articles, enhancing portfolio management.
Geometric approach for unsupervised word embedding alignment.
problem Learning alignment between word embeddings of source and target languages.
method Formulates alignment as domain adaptation on the manifold of doubly stochastic matrices, employing Riemannian conjugate gradient algorithm.
result Empirically outperforms state-of-the-art methods on bilingual lexicon induction tasks.
We propose a novel geometric approach for learning bilingual mappings given monolingual embeddings and a bilingual dictionary. Our approach decouples learning the transformation from the source language to the target language into (a) learning rotations for language-specific embeddings to align them to a common space, …
Sentiment analysis from news and social media predicts forex market movements.
problem Forecasting forex market movements using sentiment analysis.
method Lexicon-based analysis and Naive Bayes machine learning.
result Sentiment analysis is effective in predicting forex market movements.
Sparse reward is one of the most challenging problems in reinforcement learning (RL). Hindsight Experience Replay (HER) attempts to address this issue by converting a failed experience to a successful one by relabeling the goals. Despite its effectiveness, HER has limited applicability because it lacks a compact and un…
We consider the task of aligning two sets of points in high dimension, which has many applications in natural language processing and computer vision. As an example, it was recently shown that it is possible to infer a bilingual lexicon, without supervised data, by aligning word embeddings trained on monolingual data. …
Neural network detects sex trafficking ads from escort websites.
problem Manual identification of sex trafficking ads from escort websites is inefficient and resource-intensive.
method Ordinal regression neural network with a modified cost function and deep learning enhancements.
result Significantly improved detection accuracy on Trafficking-10K dataset.
Automated sentiment analysis and opinion mining is a complex process concerning the extraction of useful subjective information from text. The explosion of user generated content on the Web, especially the fact that millions of users, on a daily basis, express their opinions on products and services to blogs, wikis, so…
Language of Persuasion and Misrepresentation in Business Communication: A Textual Detection Approachcs.CL
The study detects deceptive language in business communication using AI.
problem Deceptive language in business communication.
method Combining classical rhetoric, communication psychology, and linguistic theory with computational textual analysis and transformer models.
result Detection accuracies of over 99% achieved in controlled settings.
This project demonstrated a methodology to estimating cooperate credibility with a Natural Language Processing approach. As cooperate transparency impacts both the credibility and possible future earnings of the firm, it is an important factor to be considered by banks and investors on risk assessments of listed firms.…
We define general linguistic intelligence as the ability to reuse previously acquired knowledge about a language's lexicon, syntax, semantics, and pragmatic conventions to adapt to new tasks quickly. Using this definition, we analyze state-of-the-art natural language understanding models and conduct an extensive empiri…
The paper improves ESG taxonomy and classifies sentences as sustainable or unsustainable.
problem Improving ESG taxonomy and classifying sentences based on ESG factors.
method For ESG taxonomy, used Sentence-BERT models. For sentence classification, combined RoBERTa with a multi-layer perceptron.
result Significant performance improvement and high accuracy in classifying sentences.
Hybrid and end-to-end models compare in syllable recognition.
problem Comparing hybrid and end-to-end models for syllable recognition.
method Traditional hybrid system (kaldi) vs. end-to-end (TensorFlow) models.
result Hybrid models with explicit syllable knowledge outperform end-to-end models.
In an effort to better understand meaning from natural language texts, we explore methods aimed at organizing lexical objects into contexts. A number of these methods for organization fall into a family defined by word ordering. Unlike demographic or spatial partitions of data, these collocation models are of special i…
Constructing Financial Sentimental Factors in Chinese Market Using Natural Language Processingq-fin.CP
In this paper, we design an integrated algorithm to evaluate the sentiment of Chinese market. Firstly, with the help of the web browser automation, we crawl a lot of news and comments from several influential financial websites automatically. Secondly, we use techniques of Natural Language Processing(NLP) under Chinese…
LLMs improve financial sentiment analysis in finance.
problem Defining and measuring financial sentiment.
method Investigation of sentiment measurement methods and LLMs.
result LLMs enhance financial sentiment analysis.
Hybrid ASR systems can model graphemes effectively using chenones, outperforming traditional methods.
problem Traditional hybrid ASR systems struggle with English's poor grapheme-phoneme correspondence.
method Leveraging tied context-dependent graphemes (chenones) to model graphemes directly.
result Chenone-based systems significantly outperform senone baselines by 4.5% to 11.1% on English datasets.
Building competitive direct acoustics-to-word models for English conversational speech recognitioncs.CL
Direct acoustics-to-word (A2W) models in the end-to-end paradigm have received increasing attention compared to conventional sub-word based automatic speech recognition models using phones, characters, or context-dependent hidden Markov model states. This is because A2W models recognize words from speech without any de…
How Much of a 10-K Matters? Aggregation-Dependent Value of Full-Text versus Risk-Factor Sentimentcs.LG
The study improves sentiment analysis of 10-K filings, revealing aggregation effects on accuracy and correlation with market outcomes.
problem Lack of sentiment analysis for 10-K filings, particularly for risk disclosures.
method Supervised lexicon-learning approach applied to 10-K filings and Item 1A risk-factor sections, trained against return and volatility labels at different levels of aggregation.
result Sentiment analysis of Item 1A sections performs better at the individual-firm level, while full-filing text is more accurate at sector and portfolio levels.
PreBit predicts Bitcoin price movements using social media and financial data.
problem Predicting extreme price movements of Bitcoin due to its volatility and speculative trading.
method Hybrid model combining FinBERT embeddings of Twitter content with candlestick data and technical indicators.
result The hybrid model can predict significant market movements with a profitable trading strategy.
Electronic health record (EHR) systems are used extensively throughout the healthcare domain. However, data interchangeability between EHR systems is limited due to the use of different coding standards across systems. Existing methods of mapping coding standards based on manual human experts mapping, dictionary mappin…
Identifies features most relevant to concept drift in data.
problem Identifying features most relevant to concept drift.
method Distinguishing between drift inducing and faithfully drifting features; deriving minimal subsets of features to characterize drift.
result Derives a detection algorithm for concept drift.
New method evaluates feature interactions using orthogonal variance decomposition.
problem Feature selection fails to account for interactions between features.
method Orthogonal variance decomposition to evaluate feature subsets considering interactions.
result Our method accurately identifies relevant features and improves model accuracy.
Paper predicts EEG features from acoustic features using RNN and GAN.
problem Predicting EEG features from acoustic features.
method Recurrent Neural Network (RNN) and Generative Adversarial Network (GAN).
result Lower RMSE and normalized RMSE values compared to generating acoustic features from EEG features.
Relative Feature Importancestat.ML
Introduces RFI for assessing feature importance relative to any subset of features.
problem Lack of nuanced feature importance computation.
method Generalizes PFI and CFI to assess relative feature importance.
result Derives general interpretation rules for RFI.
A single pre-trained agent guides feature selection using knockoffs.
problem Feature selection challenges in AI-readiness of data.
method Generates knockoff features and uses reinforcement learning.
result Optimal feature subset identified with reduced dependency on target variable.
Feature networks link ML features via graph structure for enhanced learning.
problem Enhancing feature expressiveness and learning efficiency in machine learning.
method Graph representation of feature vectors, leveraging Fourier and functional analysis.
result Feature networks enable novel, complex feature dependencies.
Approach for selecting features by discarding nuisance and correlated ones.
problem Large datasets with correlated and nuisance features.
method Laplacian score criterion, autoencoder architecture, concrete layer.
result Outperforms similar approaches in clustering performance.
New stability measures for similar features improve feature selection accuracy.
problem Existing stability measures fail to distinguish similar features in highly correlated datasets.
method Introduce new adjusted stability measures that consider feature similarities.
result One new stability measure considers highly similar features as interchangeable.
Counterexamples show HSIC feature selection misses critical features.
problem Feature selection using HSIC misses important features.
method Feature selection via HSIC maximization.
result HSIC feature selection can miss critical features.