## 4 Results and Discussion
### Results of Sentiment Lexicon from Hotel Reviews
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Terms** & **Tag** & **Number of Words** & \(\mathbf{e^{+}}\) & \(\mathbf{e^{-}}\) \\ \hline adequate & a & 27,210 & 0.0795 & 0.0682 \\ close & a & 2257 & 0.1810 & 0.0697 \\ abusive & a & 120 & 0 & 0.8750 \\ affable & a & 115 & 0.6250 & 0 \\ inspirational & a & 75 & 0.6250 & 0 \\ trusty & a & 52 & 0.5000 & 0 \\ accomplished & a & 51 & 0.4432 & 0 \\ mannerly & a & 44 & 0.7500 & 0 \\ stubborn & a & 40 & 0 & 0.6667 \\ immune & a & 38 & 0.0900 & 0.0950 \\ scramble & n & 213 & 0 & 0.0833 \\ defect & n & 95 & 0 & 0.0950 \\ blind & n & 68 & 0 & 0.0300 \\ despair & n & 44 & 0.0833 & 0.3750 \\ easiness & n & 38 & 0.0568 & 0.2045 \\ unwillingness & n & 33 & 0 & 0.5000 \\ enhancement & n & 32 & 0.3750 & 0 \\ prejudice & n & 30 & 0 & 0.8750 \\ horribly & r & 508 & 0 & 0.7500 \\ painfully & r & 506 & 0 & 0.0833 \\ marvellously & r & 70 & 0.5000 & 0.1250 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Candidate sentiment words with tag, frequency and initial sentiment value. Based on the PolarityRank algorithm, \(PR^{+}\) and \(PR^{-}\) of each candidate sentiment word can be estimated by Equations (1) and (2). The PolarityRank algorithm propagation process stopped until convergence. Additionally, \(SO\) of each candidate sentiment word can be calculated by Equation (3). According to the results, we can see the \(SO\) of some candidate sentiment words is equal to zero. The word with a zero \(SO\) is dropped because it is neutral without sentiment polarity. Finally, the sentiment lexicon composed of 5837 sentiment words with nonzero \(SO\) is created for attribute sentiment analysis. Due to space limitations, only the results of top 10 positive and negative sentiment words are shown in Table 6.  
From the results of sentiment lexicon, we find that some words that may not be used in daily life, but express emotions are identified. These less-common words are identified from numerous user-generated data of hotel domain through PolarityRank algorithm. This sentiment lexicon preserves some terms particular to the hotel domain, and hence it is a preferable choice to be used for sentiment analysis of hotel attributes to ensure greater accuracy [67]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sentiment Words** & **Tag** & **Number of Words** & \(PR^{+}\) & \(PR^{-}\) & \(SO\) \\ \hline \hline \multirow{2}{*}{deferentially} & r & 33 & 0.8250 & 0.0051 & 4.9382 \\  & n & 45 & 1.2375 & 0.0077 & 4.9380 \\ \multirow{2}{*}{unpaired} & a & 44 & 1.8582 & 0.0134 & 4.9282 \\  & a & 32 & 1.8582 & 0.0135 & 4.9279 \\ \multirow{2}{*}{toponch} & a & 33 & 1.5494 & 0.0121 & 4.9224 \\  & a & 35 & 1.2398 & 0.0099 & 4.9205 \\ \multirow{2}{*}{varotious} & n & 41 & 1.5504 & 0.0132 & 4.9157 \\  & n & 43 & 1.5505 & 0.0132 & 4.9156 \\ \multirow{2}{*}{amusingly} & r & 32 & 1.8606 & 0.0159 & 4.9153 \\ \multirow{2}{*}{rosy} & a & 48 & 1.5389 & 0.0139 & 4.9103 \\ \multirow{2}{*}{unstiness} & n & 31 & 0.0128 & 2.0339 & \(-\)4.9372 \\  & a & 33 & 0.0119 & 1.7442 & \(-\)4.9321 \\ \multirow{2}{*}{unemployed} & a & 34 & 0.0104 & 1.4540 & \(-\)4.9290 \\ \multirow{2}{*}{untypical} & a & 31 & 0.0129 & 1.7452 & \(-\)4.9265 \\ \multirow{2}{*}{malodorous} & a & 42 & 0.0163 & 2.0373 & \(-\)4.9206 \\  & a & 32 & 0.0140 & 1.7464 & \(-\)4.9203 \\ \multirow{2}{*}{p prejudice} & n & 30 & 0.0170 & 2.0380 & \(-\)4.9174 \\ \multirow{2}{*}{damage} & v & 30 & 0.0149 & 1.7472 & \(-\)4.9155 \\ \multirow{2}{*}{ungraded} & a & 52 & 0.0124 & 1.4560 & \(-\)4.9154 \\  & a & 33 & 0.0177 & 2.0387 & \(-\)4.9141 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: TOP 10 positive and negative sentiment words.