## 3 Detection Approaches Review
\begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Reference** & **Technique** \\ \hline
[10] & Sentence-level sentiment analysis based on lexicon and syntactic structures. Document-level sentiment analysis using a weighted sum of the polarities of sentences within the document. \\ \hline
[11] & Part of Speech (POS) tagging. Frequent feature generation using association rule mining. Feature pruning as pruning meaningless and redundant features. Opinion words extraction, extracting words that are adjacent to frequent features and are adjectives that modify the feature. Infrequent feature identification using opinion words to find the nearest noun/noun phrase of the opinion word. \\ \hline
[12] & Conditional Random Fields (CRFs) for extracting aspects. Features: token string text, POS tag of the token text, label of existence of short dependency path (between the token and opinion expressions), word distance label (token appear in the closest word distance to an opinion expression or not), opinion sentence label (does the token appear in a sentence with opinion expressions?). \\ \hline
[13] & Multi-aspect sentence labeling: Latent Dirichlet Allocation (LDA), Multi-grain LDA (MG-LDA), Segmented Topic Model (STM) and Local LDA models weakly supervised using seed words, supervised Support Vector Machine (SVM) and a majority baseline that assigns the most common aspect label. For multi-aspect rating prediction with indirect supervision: LDA, MG-LDA, STM, and local LDA weakly supervised with seed words are used to label sentences with aspects, and a Support Vector Regression (SVR) model is trained with the combined vectors of each entity and their overall ratings. Supervised multi-aspect rating prediction: Perceptron Ranking (PRank) and linear SVR are used, trained with and without features derived from LDA, MG-LDA, STM, and local LDA, that do not make use of seed words, and trained with unigram baseline features. \\ \hline
[14] & Feature extraction: the algorithm extracts frequent noun phrases from parsed reviews. It also examines opinion phrases associated with explicit features in order to extract implicit properties. Finding opinion phrases: if there is an explicit feature in a sentence, the algorithm applies extraction rules to find opinion phrases. Each head word, together with its modifiers, is returned as a potential opinion phrase. Opinion phrases polarity detection: relaxation labeling. \\ \hline
[15] & POS tagging and shallow parser. Syntactic parsing and sentiment lexicon used for sentiment analysis and relating sentiment expressions to subjects. \\ \hline
[16] & CRFs. Joint extraction of opinions and object features. \\ \hline
[17] & Linear SVMs and Logistic Regression (LR) for learning Ajective Noun pairs (ANPs) detectors for visual sentiment analysis. Visual Sentiment Ontology (VSO) based on ANPs. SentiBank, visual concept detection library that can detect 1200 ANPs in images using ANP detectors, based on VSO. \\ \hline
[18] & Progressive Convolutional Neural Network (PCNN) for image sentiment classification trained using weakly labeled data. Data from the output of the model is used to fine-tune it. Previously trained CNNs are fine-tuned with a small set of manually labeled images for addressing domain transfer. \\ \hline
[19] & Automatic Speech Recognition (ASR) for obtaining transcriptions of videos. POS tagging for extracting text-based sentiment features. A Maximum Entropy (ME) model with feature tuning for sentiment classification. \\ \hline
[20] & Algorithm for emotion classification using region switching between Vowel-like Regions (VLR) and non-VLRs from audio data. \\ \hline
[21] & Sparse autoencoder-based feature transfer learning method, using a single-layer autoencoder to find a common structure in small target data and then applying such structure to reconstruct source data. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Techniques of detection approaches.