With the advancements in internet facilities,people are more inclined towards the use of online services.The service providers shelve their items for e-users.These users post their feedbacks,reviews,ratings,etc.after ...With the advancements in internet facilities,people are more inclined towards the use of online services.The service providers shelve their items for e-users.These users post their feedbacks,reviews,ratings,etc.after the use of the item.The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items.Sentiment Analysis(SA)is a technique that performs such decision analysis.This research targets the ranking and rating through sentiment analysis of these reviews,on different aspects.As a case study,Songs are opted to design and test the decision model.Different aspects of songs namely music,lyrics,song,voice and video are picked.For the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a dataset.Different machine learning algorithms—Naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are applied.ANN performed the best with 74.99%accuracy.Results are validated using K-Fold.展开更多
Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-base...Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-based aspect-level sentiment classification model. Self-attention, aspectual word multi-head attention and dependent syntactic relations are fused and the node representations are enhanced with graph convolutional networks to enable the model to fully learn the global semantic and syntactic structural information of sentences. Experimental results show that the model performs well on three public benchmark datasets Rest14, Lap14, and Twitter, improving the accuracy of sentiment classification.展开更多
Hydrological dynamics affect water levels and thus affecting ecosystem structure and functions. Lake levels in tropical ecosystems affect phosphorous input through runoff from adjacent watersheds. The resultant biolog...Hydrological dynamics affect water levels and thus affecting ecosystem structure and functions. Lake levels in tropical ecosystems affect phosphorous input through runoff from adjacent watersheds. The resultant biological community, water and sediment quality of the lakes due to water level changes is a reflection of the geology of the area and the anthropogenic activities in the watershed. The study conducted between January 2018 and December 2019 was to explore relationships between the phosphorous input and Water Level Fluctuations (WLF) recorded by Water Resource Authority (WRA). Lake water samples were analyzed in the laboratory for phosphorous using molybdenum blue-ascorbic method and recorded using spectrophotometer. Chlorophyll-<em>a</em> was determined by extracting a filtered sample with 15 ml acetone and incubating overnight and thereafter read using a double beam spectrophotometer. Total Suspended Solids (TSS) was determined by filtering 200 ml of a water sample and dried overnight at 105<span style="white-space:nowrap;">°</span>C. The lowest and highest phosphorous concentrations recorded were 0.2 mg/l and 0.42 mg/l at NST7 and NST2, respectively. Measurements of Chlorophyll-<em>a</em> were 0.32 mg/l and 0.42 mg/l at NST9 and NST2, respectively. Secchi transparency measurements were 32.9 cm at NST3 and 84 cm at NST1. The highest and lowest TSS concentrations were 0.14 mg/l and 0.13 mg/l at NTS1 and NST8, respectively. The hydrodynamic regime in most tropical lakes plays a significant role in the re-reaction of phosphorous that consequently influences productivity. Tropical lakes have extreme lake level fluctuations which accelerate the production process. The influence of water level changes on aquatic productivity is crucial in most tropical lakes and should be taken into consideration when assessing the environmental impacts.展开更多
现有方面级情感分析研究大多数往往从文本数据本身进行情感分析,而没有充分利用领域知识,忽略了语义依存信息的重要性,使得方面表示受噪声信息影响严重,出现噪声词注意权重高的可能。针对以上问题,结合领域知识,提出了一种剪枝算法和语...现有方面级情感分析研究大多数往往从文本数据本身进行情感分析,而没有充分利用领域知识,忽略了语义依存信息的重要性,使得方面表示受噪声信息影响严重,出现噪声词注意权重高的可能。针对以上问题,结合领域知识,提出了一种剪枝算法和语义-注意力机制相结合的方法(Pruning And Semantic At tention,PASA)针对服务领域特定方面进行情感分类。方法一方面结合领域知识对文本对应的语义依存树进行剪枝实现方面信息降噪,另一方面,通过利用语义-注意力机制进行增强并精确捕获方面的上下文描述信息,从而实现对方面情感极性的判断。为了验证所提出方法的正确性和有效性,在物流数据集、酒店评论数据集及SemEval 2014的Restaurant数据集进行了大量实验,结果表明,所提出的方法相对于其它方法具有明显优势,在垂直领域具有较好的应用前景。展开更多
文摘With the advancements in internet facilities,people are more inclined towards the use of online services.The service providers shelve their items for e-users.These users post their feedbacks,reviews,ratings,etc.after the use of the item.The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items.Sentiment Analysis(SA)is a technique that performs such decision analysis.This research targets the ranking and rating through sentiment analysis of these reviews,on different aspects.As a case study,Songs are opted to design and test the decision model.Different aspects of songs namely music,lyrics,song,voice and video are picked.For the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a dataset.Different machine learning algorithms—Naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are applied.ANN performed the best with 74.99%accuracy.Results are validated using K-Fold.
文摘Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-based aspect-level sentiment classification model. Self-attention, aspectual word multi-head attention and dependent syntactic relations are fused and the node representations are enhanced with graph convolutional networks to enable the model to fully learn the global semantic and syntactic structural information of sentences. Experimental results show that the model performs well on three public benchmark datasets Rest14, Lap14, and Twitter, improving the accuracy of sentiment classification.
文摘Hydrological dynamics affect water levels and thus affecting ecosystem structure and functions. Lake levels in tropical ecosystems affect phosphorous input through runoff from adjacent watersheds. The resultant biological community, water and sediment quality of the lakes due to water level changes is a reflection of the geology of the area and the anthropogenic activities in the watershed. The study conducted between January 2018 and December 2019 was to explore relationships between the phosphorous input and Water Level Fluctuations (WLF) recorded by Water Resource Authority (WRA). Lake water samples were analyzed in the laboratory for phosphorous using molybdenum blue-ascorbic method and recorded using spectrophotometer. Chlorophyll-<em>a</em> was determined by extracting a filtered sample with 15 ml acetone and incubating overnight and thereafter read using a double beam spectrophotometer. Total Suspended Solids (TSS) was determined by filtering 200 ml of a water sample and dried overnight at 105<span style="white-space:nowrap;">°</span>C. The lowest and highest phosphorous concentrations recorded were 0.2 mg/l and 0.42 mg/l at NST7 and NST2, respectively. Measurements of Chlorophyll-<em>a</em> were 0.32 mg/l and 0.42 mg/l at NST9 and NST2, respectively. Secchi transparency measurements were 32.9 cm at NST3 and 84 cm at NST1. The highest and lowest TSS concentrations were 0.14 mg/l and 0.13 mg/l at NTS1 and NST8, respectively. The hydrodynamic regime in most tropical lakes plays a significant role in the re-reaction of phosphorous that consequently influences productivity. Tropical lakes have extreme lake level fluctuations which accelerate the production process. The influence of water level changes on aquatic productivity is crucial in most tropical lakes and should be taken into consideration when assessing the environmental impacts.
文摘现有方面级情感分析研究大多数往往从文本数据本身进行情感分析,而没有充分利用领域知识,忽略了语义依存信息的重要性,使得方面表示受噪声信息影响严重,出现噪声词注意权重高的可能。针对以上问题,结合领域知识,提出了一种剪枝算法和语义-注意力机制相结合的方法(Pruning And Semantic At tention,PASA)针对服务领域特定方面进行情感分类。方法一方面结合领域知识对文本对应的语义依存树进行剪枝实现方面信息降噪,另一方面,通过利用语义-注意力机制进行增强并精确捕获方面的上下文描述信息,从而实现对方面情感极性的判断。为了验证所提出方法的正确性和有效性,在物流数据集、酒店评论数据集及SemEval 2014的Restaurant数据集进行了大量实验,结果表明,所提出的方法相对于其它方法具有明显优势,在垂直领域具有较好的应用前景。
